KaggleCompetition- Microsoft Malware prediction AUC score-0.676

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
df_dtype={'MachineIdentifier': 'category',
 'ProductName': 'category',
 'EngineVersion': 'category',
 'AppVersion': 'category',
 'AvSigVersion': 'category',
 'Platform': 'category',
 'Processor': 'category',
 'OsVer': 'category',
 'OsPlatformSubRelease': 'category',
 'OsBuildLab': 'category',
 'SkuEdition': 'category',
 'PuaMode': 'category',
 'SmartScreen': 'category',
 'Census_MDC2FormFactor': 'category',
 'Census_DeviceFamily': 'category',
 'Census_ProcessorClass': 'category',
 'Census_PrimaryDiskTypeName': 'category',
 'Census_ChassisTypeName': 'category',
 'Census_PowerPlatformRoleName': 'category',
 'Census_InternalBatteryType': 'category',
 'Census_OSVersion': 'category',
 'Census_OSArchitecture': 'category',
 'Census_OSBranch': 'category',
 'Census_OSEdition': 'category',
 'Census_OSSkuName': 'category',
 'Census_OSInstallTypeName': 'category',
 'Census_OSWUAutoUpdateOptionsName': 'category',
 'Census_GenuineStateName': 'category',
 'Census_ActivationChannel': 'category',
 'Census_FlightRing': 'category',
 'RtpStateBitfield': 'float16',
 'DefaultBrowsersIdentifier': 'float16',
 'AVProductsInstalled': 'float16',
 'AVProductsEnabled': 'float16',
 'OrganizationIdentifier': 'float16',
 'GeoNameIdentifier': 'float16',
 'IsProtected': 'float16',
 'SMode': 'float16',
 'IeVerIdentifier': 'float16',
 'Firewall': 'float16',
 'Census_OEMNameIdentifier': 'float16',
 'Census_ProcessorCoreCount': 'float16',
 'Census_ProcessorManufacturerIdentifier': 'float16',
 'Census_ProcessorModelIdentifier': 'float16',
 'Census_InternalPrimaryDiagonalDisplaySizeInInches': 'float16',
 'Census_InternalPrimaryDisplayResolutionHorizontal': 'float16',
 'Census_InternalPrimaryDisplayResolutionVertical': 'float16',
 'Census_OSInstallLanguageIdentifier': 'float16',
 'Census_IsFlightingInternal': 'float16',
 'Census_IsFlightsDisabled': 'float16',
 'Census_ThresholdOptIn': 'float16',
 'Census_FirmwareManufacturerIdentifier': 'float16',
 'Census_IsWIMBootEnabled': 'float16',
 'Census_IsVirtualDevice': 'float16',
 'Census_IsAlwaysOnAlwaysConnectedCapable': 'float16',
 'Wdft_IsGamer': 'float16',
 'Wdft_RegionIdentifier': 'float16',
 'AVProductStatesIdentifier': 'float32',
 'CityIdentifier': 'float32',
 'UacLuaenable': 'float32',
 'Census_OEMModelIdentifier': 'float32',
 'Census_PrimaryDiskTotalCapacity': 'float32',
 'Census_SystemVolumeTotalCapacity': 'float32',
 'Census_TotalPhysicalRAM': 'float32',
 'Census_InternalBatteryNumberOfCharges': 'float32',
 'Census_FirmwareVersionIdentifier': 'float32',
 'IsBeta': 'int32',
 'IsSxsPassiveMode': 'int32',
 'HasTpm': 'int32',
 'CountryIdentifier': 'int32',
 'LocaleEnglishNameIdentifier': 'int32',
 'OsBuild': 'int32',
 'OsSuite': 'int32',
 'AutoSampleOptIn': 'int32',
 'Census_HasOpticalDiskDrive': 'int32',
 'Census_OSBuildNumber': 'int32',
 'Census_OSBuildRevision': 'int32',
 'Census_OSUILocaleIdentifier': 'int32',
 'Census_IsPortableOperatingSystem': 'int32',
 'Census_IsSecureBootEnabled': 'int32',
 'Census_IsTouchEnabled': 'int32',
 'Census_IsPenCapable': 'int32',
 'HasDetections': 'int32'}
In [3]:
%%time
df=pd.read_csv("train.csv",dtype=df_dtype)#AUC of 6.75 for 5000000
Wall time: 4min 21s
In [4]:
df_dtype={'MachineIdentifier': 'category',
 'ProductName': 'category',
 'EngineVersion': 'category',
 'AppVersion': 'category',
 'AvSigVersion': 'category',
 'Platform': 'category',
 'Processor': 'category',
 'OsVer': 'category',
 'OsPlatformSubRelease': 'category',
 'OsBuildLab': 'category',
 'SkuEdition': 'category',
 'PuaMode': 'category',
 'SmartScreen': 'category',
 'Census_MDC2FormFactor': 'category',
 'Census_DeviceFamily': 'category',
 'Census_ProcessorClass': 'category',
 'Census_PrimaryDiskTypeName': 'category',
 'Census_ChassisTypeName': 'category',
 'Census_PowerPlatformRoleName': 'category',
 'Census_InternalBatteryType': 'category',
 'Census_OSVersion': 'category',
 'Census_OSArchitecture': 'category',
 'Census_OSBranch': 'category',
 'Census_OSEdition': 'category',
 'Census_OSSkuName': 'category',
 'Census_OSInstallTypeName': 'category',
 'Census_OSWUAutoUpdateOptionsName': 'category',
 'Census_GenuineStateName': 'category',
 'Census_ActivationChannel': 'category',
 'Census_FlightRing': 'category',
 'RtpStateBitfield': 'float16',
 'DefaultBrowsersIdentifier': 'float16',
 'AVProductsInstalled': 'float16',
 'AVProductsEnabled': 'float16',
 'OrganizationIdentifier': 'float16',
 'GeoNameIdentifier': 'float16',
 'IsProtected': 'float16',
 'SMode': 'float16',
 'IeVerIdentifier': 'float16',
 'Firewall': 'float16',
 'Census_OEMNameIdentifier': 'float16',
 'Census_ProcessorCoreCount': 'float16',
 'Census_ProcessorManufacturerIdentifier': 'float16',
 'Census_ProcessorModelIdentifier': 'float16',
 'Census_InternalPrimaryDiagonalDisplaySizeInInches': 'float16',
 'Census_InternalPrimaryDisplayResolutionHorizontal': 'float16',
 'Census_InternalPrimaryDisplayResolutionVertical': 'float16',
 'Census_OSInstallLanguageIdentifier': 'float16',
 'Census_IsFlightingInternal': 'float16',
 'Census_IsFlightsDisabled': 'float16',
 'Census_ThresholdOptIn': 'float16',
 'Census_FirmwareManufacturerIdentifier': 'float16',
 'Census_IsWIMBootEnabled': 'float16',
 'Census_IsVirtualDevice': 'float16',
 'Census_IsAlwaysOnAlwaysConnectedCapable': 'float16',
 'Wdft_IsGamer': 'float16',
 'Wdft_RegionIdentifier': 'float16',
 'AVProductStatesIdentifier': 'float32',
 'CityIdentifier': 'float32',
 'UacLuaenable': 'float32',
 'Census_OEMModelIdentifier': 'float32',
 'Census_PrimaryDiskTotalCapacity': 'float32',
 'Census_SystemVolumeTotalCapacity': 'float32',
 'Census_TotalPhysicalRAM': 'float32',
 'Census_InternalBatteryNumberOfCharges': 'float32',
 'Census_FirmwareVersionIdentifier': 'float32',
 'IsBeta': 'int32',
 'IsSxsPassiveMode': 'int32',
 'HasTpm': 'int32',
 'CountryIdentifier': 'int32',
 'LocaleEnglishNameIdentifier': 'int32',
 'OsBuild': 'int32',
 'OsSuite': 'int32',
 'AutoSampleOptIn': 'int32',
 'Census_HasOpticalDiskDrive': 'int32',
 'Census_OSBuildNumber': 'int32',
 'Census_OSBuildRevision': 'int32',
 'Census_OSUILocaleIdentifier': 'int32',
 'Census_IsPortableOperatingSystem': 'int32',
 'Census_IsSecureBootEnabled': 'int32',
 'Census_IsTouchEnabled': 'int32',
 'Census_IsPenCapable': 'int32'}
In [5]:
%%time
df_test=pd.read_csv("test.csv",dtype=df_dtype)#AUC of 6.46 for 5000000
Wall time: 4min 16s
In [6]:
solution=pd.DataFrame()
solution['MachineIdentifier']=df_test['MachineIdentifier']
In [7]:
df.shape
Out[7]:
(8921483, 83)
In [8]:
df.memory_usage(deep=True).sum()
Out[8]:
2861438459
In [9]:
df.head()
Out[9]:
MachineIdentifier ProductName EngineVersion AppVersion AvSigVersion IsBeta RtpStateBitfield IsSxsPassiveMode DefaultBrowsersIdentifier AVProductStatesIdentifier ... Census_FirmwareVersionIdentifier Census_IsSecureBootEnabled Census_IsWIMBootEnabled Census_IsVirtualDevice Census_IsTouchEnabled Census_IsPenCapable Census_IsAlwaysOnAlwaysConnectedCapable Wdft_IsGamer Wdft_RegionIdentifier HasDetections
0 0000028988387b115f69f31a3bf04f09 win8defender 1.1.15100.1 4.18.1807.18075 1.273.1735.0 0 7.0 0 NaN 53447.0 ... 36144.0 0 NaN 0.0 0 0 0.0 0.0 10.0 0
1 000007535c3f730efa9ea0b7ef1bd645 win8defender 1.1.14600.4 4.13.17134.1 1.263.48.0 0 7.0 0 NaN 53447.0 ... 57858.0 0 NaN 0.0 0 0 0.0 0.0 8.0 0
2 000007905a28d863f6d0d597892cd692 win8defender 1.1.15100.1 4.18.1807.18075 1.273.1341.0 0 7.0 0 NaN 53447.0 ... 52682.0 0 NaN 0.0 0 0 0.0 0.0 3.0 0
3 00000b11598a75ea8ba1beea8459149f win8defender 1.1.15100.1 4.18.1807.18075 1.273.1527.0 0 7.0 0 NaN 53447.0 ... 20050.0 0 NaN 0.0 0 0 0.0 0.0 3.0 1
4 000014a5f00daa18e76b81417eeb99fc win8defender 1.1.15100.1 4.18.1807.18075 1.273.1379.0 0 7.0 0 NaN 53447.0 ... 19844.0 0 0.0 0.0 0 0 0.0 0.0 1.0 1

5 rows × 83 columns

In [10]:
df.describe().T
Out[10]:
count mean std min 25% 50% 75% max
IsBeta 8921483.0 7.509962e-06 2.740421e-03 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
RtpStateBitfield 8889165.0 NaN 0.000000e+00 0.000000 7.000000 7.0 7.000000e+00 3.500000e+01
IsSxsPassiveMode 8921483.0 1.733378e-02 1.305118e-01 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
DefaultBrowsersIdentifier 433438.0 NaN NaN 1.000000 788.000000 1632.0 2.372000e+03 3.212000e+03
AVProductStatesIdentifier 8885262.0 4.948320e+04 1.379994e+04 3.000000 49480.000000 53447.0 5.344700e+04 7.050700e+04
AVProductsInstalled 8885262.0 NaN 0.000000e+00 0.000000 1.000000 1.0 2.000000e+00 7.000000e+00
AVProductsEnabled 8885262.0 NaN 0.000000e+00 0.000000 1.000000 1.0 1.000000e+00 5.000000e+00
HasTpm 8921483.0 9.879711e-01 1.090149e-01 0.000000 1.000000 1.0 1.000000e+00 1.000000e+00
CountryIdentifier 8921483.0 1.080490e+02 6.304706e+01 1.000000 51.000000 97.0 1.620000e+02 2.220000e+02
CityIdentifier 8596074.0 8.049152e+04 4.873461e+04 5.000000 36825.000000 82373.0 1.237000e+05 1.679620e+05
OrganizationIdentifier 6169965.0 NaN 0.000000e+00 1.000000 18.000000 27.0 2.700000e+01 5.200000e+01
GeoNameIdentifier 8921270.0 NaN NaN 1.000000 89.000000 181.0 2.670000e+02 2.960000e+02
LocaleEnglishNameIdentifier 8921483.0 1.228161e+02 6.932125e+01 1.000000 74.000000 88.0 1.820000e+02 2.830000e+02
OsBuild 8921483.0 1.571997e+04 2.190685e+03 7600.000000 15063.000000 16299.0 1.713400e+04 1.824400e+04
OsSuite 8921483.0 5.751534e+02 2.480847e+02 16.000000 256.000000 768.0 7.680000e+02 7.840000e+02
IsProtected 8885439.0 NaN 0.000000e+00 0.000000 1.000000 1.0 1.000000e+00 1.000000e+00
AutoSampleOptIn 8921483.0 2.891896e-05 5.377558e-03 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
SMode 8383724.0 4.649162e-04 2.104187e-02 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
IeVerIdentifier 8862589.0 NaN NaN 1.000000 111.000000 117.0 1.370000e+02 4.290000e+02
Firewall 8830133.0 NaN 0.000000e+00 0.000000 1.000000 1.0 1.000000e+00 1.000000e+00
UacLuaenable 8910645.0 1.216142e+01 9.867765e+03 0.000000 1.000000 1.0 1.000000e+00 1.677722e+07
Census_OEMNameIdentifier 8826005.0 NaN NaN 1.000000 1443.000000 2102.0 2.668000e+03 6.144000e+03
Census_OEMModelIdentifier 8819250.0 2.378578e+05 7.148446e+04 1.000000 189692.000000 247458.0 3.044180e+05 3.454980e+05
Census_ProcessorCoreCount 8880177.0 NaN 0.000000e+00 1.000000 2.000000 4.0 4.000000e+00 1.920000e+02
Census_ProcessorManufacturerIdentifier 8880170.0 NaN 0.000000e+00 1.000000 5.000000 5.0 5.000000e+00 1.000000e+01
Census_ProcessorModelIdentifier 8880140.0 NaN NaN 2.000000 1998.000000 2500.0 2.874000e+03 4.480000e+03
Census_PrimaryDiskTotalCapacity 8868467.0 2.912138e+06 4.451633e+09 0.000000 239372.000000 476940.0 9.538690e+05 8.160437e+12
Census_SystemVolumeTotalCapacity 8868481.0 3.823069e+05 3.233614e+05 0.000000 120775.000000 249500.0 4.759730e+05 4.768710e+07
Census_HasOpticalDiskDrive 8921483.0 7.718728e-02 2.668884e-01 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
Census_TotalPhysicalRAM 8840950.0 6.109390e+03 4.971148e+03 255.000000 4096.000000 4096.0 8.192000e+03 1.572864e+06
Census_InternalPrimaryDiagonalDisplaySizeInInches 8874349.0 NaN 0.000000e+00 0.700195 13.898438 15.5 1.720312e+01 1.822500e+02
Census_InternalPrimaryDisplayResolutionHorizontal 8874497.0 NaN NaN -1.000000 1366.000000 1366.0 1.920000e+03 1.228800e+04
Census_InternalPrimaryDisplayResolutionVertical 8874497.0 NaN NaN -1.000000 768.000000 768.0 1.080000e+03 8.640000e+03
Census_InternalBatteryNumberOfCharges 8652728.0 1.123782e+09 1.933305e+09 0.000000 0.000000 0.0 4.294967e+09 4.294967e+09
Census_OSBuildNumber 8921483.0 1.583483e+04 1.961743e+03 7600.000000 15063.000000 16299.0 1.713400e+04 1.824400e+04
Census_OSBuildRevision 8921483.0 9.730490e+02 2.931971e+03 0.000000 167.000000 285.0 5.470000e+02 4.173600e+04
Census_OSInstallLanguageIdentifier 8861399.0 NaN 0.000000e+00 1.000000 8.000000 9.0 2.000000e+01 3.900000e+01
Census_OSUILocaleIdentifier 8921483.0 6.046534e+01 4.499992e+01 1.000000 31.000000 34.0 9.000000e+01 1.620000e+02
Census_IsPortableOperatingSystem 8921483.0 5.452008e-04 2.334317e-02 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
Census_IsFlightingInternal 1512724.0 1.388788e-05 3.726959e-03 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
Census_IsFlightsDisabled 8760960.0 1.007318e-05 3.173828e-03 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
Census_ThresholdOptIn 3254158.0 2.508163e-04 1.582336e-02 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
Census_FirmwareManufacturerIdentifier 8738226.0 NaN NaN 2.000000 142.000000 500.0 5.560000e+02 1.092000e+03
Census_FirmwareVersionIdentifier 8761350.0 3.268055e+04 2.112612e+04 3.000000 13156.000000 33070.0 5.243600e+04 7.210500e+04
Census_IsSecureBootEnabled 8921483.0 4.860229e-01 4.998046e-01 0.000000 0.000000 0.0 1.000000e+00 1.000000e+00
Census_IsWIMBootEnabled 3261780.0 2.980232e-07 5.459785e-04 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
Census_IsVirtualDevice 8905530.0 7.202148e-03 8.453369e-02 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
Census_IsTouchEnabled 8921483.0 1.255431e-01 3.313338e-01 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
Census_IsPenCapable 8921483.0 3.807091e-02 1.913675e-01 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
Census_IsAlwaysOnAlwaysConnectedCapable 8850140.0 NaN 0.000000e+00 0.000000 0.000000 0.0 0.000000e+00 1.000000e+00
Wdft_IsGamer 8618032.0 NaN 0.000000e+00 0.000000 0.000000 0.0 1.000000e+00 1.000000e+00
Wdft_RegionIdentifier 8618032.0 NaN 0.000000e+00 1.000000 3.000000 10.0 1.100000e+01 1.500000e+01
HasDetections 8921483.0 4.997927e-01 5.000000e-01 0.000000 0.000000 0.0 1.000000e+00 1.000000e+00
In [11]:
print("Number of Columns : ",len(df.columns))
print("Number of Rows : ",len(df))
Number of Columns :  83
Number of Rows :  8921483
In [12]:
l=df.columns[df.isnull().any()].tolist()
In [13]:
drop_list=[]
delete_rows_list=[]
treat_list=[]
for column in l:
    #print("Number of Null Values for : "+str(column)+" "+str(df[column].isnull().sum())+" "+str(df[column].isnull().sum()*100/len(df))+"%")
    if df[column].isnull().sum()*100/len(df)>=70:#dropping columns with more than 70% of missing values
        drop_list.append(column)
    elif df[column].isnull().sum()*100/len(df)<=30:#Deleting rows having columns upto 30% missing values
        delete_rows_list.append(column)
    else:#Treating null values in the remainder of the columns
        treat_list.append(column)
In [14]:
df.drop(columns=drop_list,axis=1,inplace=True)#Dropping columns with more than 60% of missing values
In [15]:
df_test.drop(columns=drop_list,axis=1,inplace=True)#Dropping columns with more than 60% of missing values
In [16]:
#df.dropna(subset=delete_rows_list,axis=0,inplace=True)
In [17]:
import statistics
In [18]:
statistics.mode(df.SmartScreen)
Out[18]:
'RequireAdmin'
In [19]:
df.SmartScreen.fillna(statistics.mode(df.SmartScreen),inplace=True)
In [20]:
#df.OrganizationIdentifier.fillna(np.nanmedian(df.OrganizationIdentifier),inplace=True)
In [21]:
df.shape
Out[21]:
(8921483, 78)
In [22]:
t=df.isnull().sum().reset_index().sort_values(ascending=False,by=[0])#.plot(kind='barh',figsize=(12,7))
In [23]:
fig,ax=plt.subplots(figsize=(12,7))
sns.barplot(x=t[t[0]>0][0],y=t[t[0]>0]['index'],ax=ax)
ax.set_title("Missing Values in features")
ax.set_xlabel("Number of Missing Values")
ax.set_ylabel("Features")
plt.show()
In [24]:
df=df[~df.SmartScreen.isin(['&#x02;','&#x01;','&#x03;'])]
In [25]:
df.shape
Out[25]:
(8920731, 78)
In [26]:
df['SmartScreen']=df.SmartScreen.str.lower()
df_test['SmartScreen']=df_test.SmartScreen.str.lower()
In [27]:
df=df[~df.SmartScreen.isin(['promt'])]

df=df[~df.SmartScreen.isin(['00000000'])]

df=df[~df.SmartScreen.isin(['enabled'])]

df=df[df.SmartScreen!='0']
In [31]:
df.SmartScreen.value_counts()
Out[31]:
requireadmin    7493205
existsnotset    1046183
off              187907
warn             135484
prompt            34534
block             22533
on                  878
Name: SmartScreen, dtype: int64
In [32]:
df_test.SmartScreen.value_counts()
Out[32]:
requireadmin     3413570
existsnotset      600446
off               163161
warn              125926
prompt             28889
block              21242
on                   939
&#x02;               404
&#x01;               267
0                      3
promprt                1
deny                   1
of                     1
requiredadmin          1
Name: SmartScreen, dtype: int64
In [33]:
df_test['SmartScreen']=df_test.SmartScreen.str.replace('&#x02;','existsnotset')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('&#x01;','existsnotset')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('0','off')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('deny','existsnotset')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('requiredadmin','existsnotset')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('of','existsnotset')
df_test['SmartScreen']=df_test.SmartScreen.str.replace('promprt','existsnotset')
In [34]:
df_test.SmartScreen.value_counts()
Out[34]:
requireadmin     3413570
existsnotset      601121
existsnotsetf     163164
warn              125926
prompt             28889
block              21242
on                   939
Name: SmartScreen, dtype: int64
In [35]:
df.Platform.unique()
Out[35]:
[windows10, windows7, windows8, windows2016]
Categories (4, object): [windows10, windows7, windows8, windows2016]
In [36]:
df[df.SmartScreen.isin(['on'])]['Platform'].value_counts()
Out[36]:
windows10      876
windows7         2
windows8         0
windows2016      0
Name: Platform, dtype: int64
In [37]:
df[df.Platform=='windows8']['SmartScreen'].value_counts()
Out[37]:
requireadmin    158471
existsnotset     33345
off               1605
prompt            1074
Name: SmartScreen, dtype: int64
In [38]:
df[df.Platform=='windows7']['SmartScreen'].value_counts()
Out[38]:
requireadmin    89306
existsnotset     4416
off               159
on                  2
warn                1
Name: SmartScreen, dtype: int64
In [39]:
df[df.Platform=='windows10']['SmartScreen'].value_counts()
Out[39]:
requireadmin    7242077
existsnotset    1008045
off              177058
warn             135483
prompt            31908
block             22533
on                  876
Name: SmartScreen, dtype: int64
In [40]:
df[df.Platform=='windows2016']['SmartScreen'].value_counts()
Out[40]:
off             9085
requireadmin    3351
prompt          1552
existsnotset     377
Name: SmartScreen, dtype: int64
In [41]:
df[df.SmartScreen=='prompt']['Platform'].value_counts()
Out[41]:
windows10      31908
windows2016     1552
windows8        1074
windows7           0
Name: Platform, dtype: int64
In [42]:
df.OsBuild.nunique()
Out[42]:
76
In [43]:
df_test.OsBuild.nunique()
Out[43]:
78
In [44]:
df.OsBuild.value_counts().head()
Out[44]:
17134    3915214
16299    2503359
15063     780211
14393     730782
10586     411599
Name: OsBuild, dtype: int64
In [45]:
df[df.OsVer.str.startswith('6.1.')]['Platform'].value_counts()
Out[45]:
windows7       93884
windows8           0
windows2016        0
windows10          0
Name: Platform, dtype: int64
In [46]:
df[df.OsVer.str.startswith('6.3.')]['Platform'].value_counts()
Out[46]:
windows8       194495
windows7            0
windows2016         0
windows10           0
Name: Platform, dtype: int64
In [47]:
df[df.OsVer.str.startswith('10.0.0.0')]['Platform'].value_counts()
Out[47]:
windows10      8617439
windows2016      14365
windows8             0
windows7             0
Name: Platform, dtype: int64
In [48]:
categorical_columns=['ProductName', 'EngineVersion', 'AppVersion','AvSigVersion','RtpStateBitfield','AVProductStatesIdentifier','CountryIdentifier', 'CityIdentifier','OrganizationIdentifier', 'GeoNameIdentifier','LocaleEnglishNameIdentifier', 'Platform', 'Processor', 'OsVer','OsBuild', 'OsSuite', 'OsPlatformSubRelease', 'OsBuildLab','SkuEdition','IeVerIdentifier', 'SmartScreen','UacLuaenable','Census_MDC2FormFactor','Census_DeviceFamily', 'Census_OEMNameIdentifier','Census_OEMModelIdentifier','Census_ProcessorManufacturerIdentifier','Census_ProcessorModelIdentifier','Census_PrimaryDiskTypeName','Census_ChassisTypeName','Census_PowerPlatformRoleName','Census_OSVersion', 'Census_OSArchitecture', 'Census_OSBranch','Census_OSBuildNumber', 'Census_OSBuildRevision', 'Census_OSEdition','Census_OSSkuName', 'Census_OSInstallTypeName', 'Census_OSInstallLanguageIdentifier', 'Census_OSUILocaleIdentifier','Census_OSWUAutoUpdateOptionsName','Census_GenuineStateName', 'Census_ActivationChannel','Census_FlightRing','Census_FirmwareManufacturerIdentifier','Census_FirmwareVersionIdentifier','Wdft_RegionIdentifier']

numerical_columns=['Census_ProcessorCoreCount','Census_PrimaryDiskTotalCapacity','Census_SystemVolumeTotalCapacity','Census_TotalPhysicalRAM','Census_InternalPrimaryDiagonalDisplaySizeInInches','Census_InternalPrimaryDisplayResolutionHorizontal','Census_InternalPrimaryDisplayResolutionVertical','Census_InternalBatteryNumberOfCharges']

binary_columns=list(set(df.columns)-set(categorical_columns)-set(numerical_columns)-set(['MachineIdentifier','HasDetections']))
In [49]:
df[categorical_columns]=df[categorical_columns].astype('category')
In [50]:
#Creating a dictionary for missing values in 
column_value_replacements={}
for col in df.columns[1:]:
    if col in df.select_dtypes(include=['category','object']).columns:
        column_value_replacements[col]=statistics.mode(df[df[col].notna()][col])
    else:
        column_value_replacements[col]=np.nanmedian(df[col])
In [51]:
country_city={}
for val in df['CountryIdentifier'].unique():
    country_city[val]=statistics.mode(df[df.CountryIdentifier==val]['CityIdentifier'])
In [52]:
%%time
df['CityIdentifier']=[country_city[y] if pd.isnull(x) else x for x,y in zip(df.CityIdentifier,df.CountryIdentifier)]
Wall time: 9 s
In [53]:
%%time
df_test['CityIdentifier']=[country_city[y] if pd.isnull(x) else x for x,y in zip(df_test.CityIdentifier,df_test.CountryIdentifier)]
Wall time: 7.34 s
In [54]:
df_test[categorical_columns]=df_test[categorical_columns].astype('category')
In [55]:
%%time
for col in df_test.columns[df_test.isnull().any()]:
    df_test[col].fillna(column_value_replacements[col],inplace=True)
Wall time: 17.1 s
In [56]:
%%time
for col in df.columns[df.isnull().any()]:
    df[col].fillna(column_value_replacements[col],inplace=True)
Wall time: 19.5 s
In [57]:
def reduce_memory(df):
    initial=df.memory_usage(deep=True).sum()
    print("Changing Datatypes please wait.......")
    for col in df.select_dtypes(include=['int64','int32','float64','float32','int16']).columns:
        try:
            if df[col].dtype in ['int8','int16','int32','int64']:
                if (np.max(df[col])<np.iinfo('int8').max)&(np.min(df[col])>np.iinfo('int8').min):
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'int8')
                    df[col]=df[col].astype('int8')
                elif (np.max(df[col])<np.iinfo('int16').max)&(np.min(df[col])>np.iinfo('int16').min):
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'int16')
                    df[col]=df[col].astype('int16')
                elif (np.max(df[col])<np.iinfo('int32').max)&(np.min(df[col])>np.iinfo('int32').min):
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'int32')
                    df[col]=df[col].astype('int32')
                else:
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'int64')
                    df[col]=df[col].astype('int64')
            else:
                if (np.max(df[col])<np.finfo('float16').max)&(np.min(df[col])>np.finfo('float16').min):
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'float16')
                    df[col]=df[col].astype('float16')
                elif (np.max(df[col])<np.finfo('float32').max)&(np.min(df[col])>np.finfo('float32').min):
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'float32')
                    df[col]=df[col].astype('float32')
                else:
                    #print(str(col)+" changing datatype from "+str(df[col].dtype)+" to " +'float64')
                    df[col]=df[col].astype('float64')
        except:
            print("Exception for column ",col)
            continue
    final=df.memory_usage(deep=True).sum()
    print("Datatypes updated and memory usage is reduced by :",initial-final)
    return df
In [58]:
def my_plots(df,col):
    osver_10=df[col].value_counts().reset_index()['index'].head(10).tolist()

    #df[df.HasDetections==1][col].value_counts()

    t1=df[df.HasDetections==1][col].value_counts().reset_index()

    firmware_affected=t1[t1['index'].isin(osver_10)][col].tolist()

    t2=df[df.HasDetections==0][col].value_counts().reset_index()
    firmware_unaffected=t2[t2['index'].isin(osver_10)][col].tolist()

    index=np.arange(len(osver_10))

    #engine_affected

    width=0.3

    plt.figure(figsize=(12,6))
    plt.barh(index,firmware_affected,width,color='r',label='Affected')
    plt.barh(index+width,firmware_unaffected,width,color='g',label='Unaffected')
    plt.ylabel(col)
    plt.xlabel('Count')
    plt.yticks(index+width/2,osver_10,rotation=0)
    plt.legend(loc='best')
    plt.show()
In [59]:
def year_month(df):
    df['OsBuildLab']=df.OsBuildLab.astype('str')
    temp=df.OsBuildLab.str.split(".",expand=True)

    temp=temp[4].str.split("-",expand=True)

    time_train=pd.DataFrame()

    time_train['Year']=temp[0].astype('int')//10000

    time_train['Month']=(temp[0].astype('int')%10000)//100

    time_train['Day']=(temp[0].astype('int')%10000)%100

    #time_train['Year'].value_counts()

    #time_train['Month'].value_counts()

    #time_train['Day'].value_counts()
    #All Day id greater than 31 belong to November and December 2017.
    #temp[temp[0].str.endswith('53')]

    #temp[temp[0].str.endswith('57')]

    #temp[temp[0].str.endswith('77')]

    #temp[temp[0].str.endswith('32')]

    #temp[temp[0].str.endswith('78')]

    df['Year']=time_train['Year']
    df['Month']=time_train['Month']
    return df
In [60]:
%%time
df=year_month(df)
Wall time: 1min 45s
In [61]:
df_test.loc[6529507,'OsBuildLab']=df_test.loc[6529507,'OsBuildLab'].replace('*','.')
In [62]:
#temp=df_test.OsBuildLab.str.split(".",expand=True)

#temp=temp[4].str.split("-",expand=True)
In [63]:
%%time
df_test=year_month(df_test)
Wall time: 1min 47s
In [64]:
#df_train=df[~df.isnull().any(axis=1)]
In [65]:
sns.countplot(data=df,x='Year',hue='HasDetections')
plt.show()
In [66]:
sns.countplot(data=df,x='Year')
plt.show()
In [69]:
sns.countplot(data=df_test,x='Year')
plt.show()
In [70]:
sns.countplot(data=df,x='Month',hue='HasDetections')
plt.show()
In [71]:
sns.countplot(data=df,x='Month')
plt.show()
In [72]:
sns.countplot(data=df_test,x='Month')
plt.show()
In [67]:
df[(df.Month!=4)&(df.Year!=18)]['HasDetections'].value_counts()
Out[67]:
0    1616458
1    1425774
Name: HasDetections, dtype: int64
In [68]:
#df[df.Year==18]['Census_InternalBatteryNumberOfCharges'].boxplot()
In [81]:
sns.boxplot(data=df[df.Year==15],y='Census_InternalBatteryNumberOfCharges',x='HasDetections')
Out[81]:
<matplotlib.axes._subplots.AxesSubplot at 0x1ce7f2dc320>
In [86]:
for val in df.Year.unique():
    sns.countplot(data=df[df.Year==val],x='Month',hue='HasDetections')
    plt.title("Year "+str(val))
    plt.show()
In [69]:
df=pd.get_dummies(data=df,columns=['Month','Year'],dtype='int8')
df_test=pd.get_dummies(data=df_test,columns=['Month','Year'],dtype='int8')
In [70]:
month_columns=df.columns[df.columns.str.startswith('Month')]
year_columns=df.columns[df.columns.str.startswith('Year')]
In [71]:
month_columns
Out[71]:
Index(['Month_1', 'Month_2', 'Month_3', 'Month_4', 'Month_5', 'Month_6',
       'Month_7', 'Month_8', 'Month_9', 'Month_10', 'Month_11', 'Month_12'],
      dtype='object')
In [72]:
%%time
#i=1
for year in year_columns:
    for month in month_columns:
        new_col='Interaction_MY'+str(year)+str(month)
        df[new_col]=np.multiply(df[year],df[month])
        #i=i+1
Wall time: 8.2 s
In [73]:
df.head()
Out[73]:
MachineIdentifier ProductName EngineVersion AppVersion AvSigVersion IsBeta RtpStateBitfield IsSxsPassiveMode AVProductStatesIdentifier AVProductsInstalled ... Interaction_MYYear_18Month_3 Interaction_MYYear_18Month_4 Interaction_MYYear_18Month_5 Interaction_MYYear_18Month_6 Interaction_MYYear_18Month_7 Interaction_MYYear_18Month_8 Interaction_MYYear_18Month_9 Interaction_MYYear_18Month_10 Interaction_MYYear_18Month_11 Interaction_MYYear_18Month_12
0 0000028988387b115f69f31a3bf04f09 win8defender 1.1.15100.1 4.18.1807.18075 1.273.1735.0 0 7.0 0 53447.0 1.0 ... 0 1 0 0 0 0 0 0 0 0
1 000007535c3f730efa9ea0b7ef1bd645 win8defender 1.1.14600.4 4.13.17134.1 1.263.48.0 0 7.0 0 53447.0 1.0 ... 0 1 0 0 0 0 0 0 0 0
2 000007905a28d863f6d0d597892cd692 win8defender 1.1.15100.1 4.18.1807.18075 1.273.1341.0 0 7.0 0 53447.0 1.0 ... 0 1 0 0 0 0 0 0 0 0
3 00000b11598a75ea8ba1beea8459149f win8defender 1.1.15100.1 4.18.1807.18075 1.273.1527.0 0 7.0 0 53447.0 1.0 ... 0 1 0 0 0 0 0 0 0 0
4 000014a5f00daa18e76b81417eeb99fc win8defender 1.1.15100.1 4.18.1807.18075 1.273.1379.0 0 7.0 0 53447.0 1.0 ... 0 1 0 0 0 0 0 0 0 0

5 rows × 220 columns

In [74]:
%%time
i=1
for year in year_columns:
    for month in month_columns:
        new_col='Interaction_MY'+str(year)+str(month)
        df_test[new_col]=np.multiply(df_test[year],df_test[month])
        i=i+1
Wall time: 7.06 s
In [76]:
#df_test.SmartScreen.replace('of','off',inplace=True)
#df_test.SmartScreen.replace('requiredadmin','requireadmin',inplace=True)
In [77]:
df['Device_PossibleOwnership']=[0 if x=='requireadmin' else 1 for x in df['SmartScreen']]
df_test['Device_PossibleOwnership']=[0 if x=='requireadmin' else 1 for x in df_test['SmartScreen']]
In [79]:
%%time
df=reduce_memory(df=df)
Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 365749684
Wall time: 17.4 s
In [80]:
%%time
df_test=reduce_memory(df=df_test)
Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 267010602
Wall time: 14.6 s
In [88]:
fig,ax=plt.subplots(figsize=(12,7))
sns.boxplot(data=df,x=categorical_columns[0],y=numerical_columns[7],hue='HasDetections',ax=ax)
Out[88]:
<matplotlib.axes._subplots.AxesSubplot at 0x2682f7f9eb8>
In [116]:
fig,ax=plt.subplots(figsize=(12,7))
sns.barplot(data=df,x=categorical_columns[0],y=numerical_columns[0],ax=ax)
plt.show()
C:\Users\gandh\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [117]:
fig,ax=plt.subplots(figsize=(12,7))
sns.barplot(data=df,x='OsPlatformSubRelease',y=numerical_columns[0],ax=ax)
plt.show()
C:\Users\gandh\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [118]:
fig,ax=plt.subplots(figsize=(12,7))
sns.barplot(data=df,x='Census_DeviceFamily',y=numerical_columns[0],ax=ax)
plt.show()
C:\Users\gandh\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [119]:
fig,ax=plt.subplots(figsize=(12,7))
sns.violinplot(data=df,x=categorical_columns[0],y=numerical_columns[0],hue='HasDetections')
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\_methods.py:107: RuntimeWarning: overflow encountered in reduce
  arrmean = umr_sum(arr, axis, dtype, keepdims=True)
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\function_base.py:133: RuntimeWarning: invalid value encountered in multiply
  y *= step
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\function_base.py:142: RuntimeWarning: invalid value encountered in add
  y += start
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\_methods.py:28: RuntimeWarning: invalid value encountered in reduce
  return umr_maximum(a, axis, None, out, keepdims, initial)
C:\Users\gandh\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[119]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f8f62342b0>
In [120]:
fig,ax=plt.subplots(figsize=(12,7))
sns.violinplot(data=df,x=categorical_columns[0],y=numerical_columns[4],hue='HasDetections')
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\_methods.py:107: RuntimeWarning: overflow encountered in reduce
  arrmean = umr_sum(arr, axis, dtype, keepdims=True)
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\function_base.py:133: RuntimeWarning: invalid value encountered in multiply
  y *= step
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\function_base.py:142: RuntimeWarning: invalid value encountered in add
  y += start
C:\Users\gandh\Anaconda3\lib\site-packages\numpy\core\_methods.py:28: RuntimeWarning: invalid value encountered in reduce
  return umr_maximum(a, axis, None, out, keepdims, initial)
C:\Users\gandh\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[120]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f932940ac8>
In [105]:
df[numerical_columns].describe().T
Out[105]:
count mean std min 25% 50% 75% max
Census_ProcessorCoreCount 8920724.0 NaN 0.000000e+00 1.000000 2.000000 4.0 4.000000e+00 1.920000e+02
Census_PrimaryDiskTotalCapacity 8920724.0 2.895155e+06 4.438576e+09 0.000000 244197.000000 476940.0 9.538690e+05 8.160437e+12
Census_SystemVolumeTotalCapacity 8920724.0 3.816120e+05 3.230770e+05 0.000000 120827.000000 249500.0 4.759650e+05 4.768710e+07
Census_TotalPhysicalRAM 8920724.0 6.091263e+03 4.951270e+03 255.000000 4096.000000 4096.0 8.192000e+03 1.572864e+06
Census_InternalPrimaryDiagonalDisplaySizeInInches 8920724.0 NaN 0.000000e+00 0.700195 13.898438 15.5 1.720312e+01 1.822500e+02
Census_InternalPrimaryDisplayResolutionHorizontal 8920724.0 NaN NaN -1.000000 1366.000000 1366.0 1.920000e+03 1.228800e+04
Census_InternalPrimaryDisplayResolutionVertical 8920724.0 NaN NaN -1.000000 768.000000 768.0 1.080000e+03 8.640000e+03
Census_InternalBatteryNumberOfCharges 8920724.0 1.089876e+09 1.925254e+09 0.000000 0.000000 0.0 4.294967e+09 4.294967e+09
In [106]:
np.min(df.Census_TotalPhysicalRAM)
Out[106]:
255.0
In [107]:
df[df.Census_TotalPhysicalRAM>409600].shape
Out[107]:
(34, 219)
In [108]:
df[df.Census_TotalPhysicalRAM>509600][['OsPlatformSubRelease','AVProductStatesIdentifier','RtpStateBitfield','Census_DeviceFamily','Wdft_IsGamer','AVProductsEnabled','HasDetections']]
Out[108]:
OsPlatformSubRelease AVProductStatesIdentifier RtpStateBitfield Census_DeviceFamily Wdft_IsGamer AVProductsEnabled HasDetections
77402 rs4 53447.0 7.0 Windows.Desktop 0.0 1.0 0
282900 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
359909 rs1 53447.0 7.0 Windows.Desktop 0.0 1.0 0
423049 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
843490 rs4 53447.0 7.0 Windows.Desktop 0.0 1.0 1
844448 rs4 53447.0 7.0 Windows.Desktop 0.0 1.0 0
912233 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
942736 rs1 53447.0 7.0 Windows.Server 0.0 1.0 1
1119826 rs4 35379.0 0.0 Windows.Desktop 0.0 1.0 0
1827338 rs1 53447.0 7.0 Windows.Server 0.0 1.0 1
1866798 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
1882995 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
1905786 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
2382892 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
2404851 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
3168236 rs4 53447.0 7.0 Windows.Desktop 0.0 1.0 1
3283213 rs1 53447.0 7.0 Windows.Server 0.0 1.0 1
3497061 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
3528025 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
3912495 rs4 53447.0 7.0 Windows.Desktop 0.0 1.0 0
3991746 rs1 53447.0 7.0 Windows.Server 0.0 1.0 1
4222767 rs4 53447.0 7.0 Windows.Desktop 0.0 1.0 0
4683372 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
4778357 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
4904555 rs4 53447.0 7.0 Windows.Desktop 1.0 1.0 1
4978744 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
5684683 rs3 53447.0 7.0 Windows.Desktop 0.0 1.0 0
6164464 rs4 53447.0 7.0 Windows.Desktop 0.0 1.0 0
6643797 rs1 53447.0 7.0 Windows.Server 0.0 1.0 0
7898158 rs4 30961.0 7.0 Windows.Desktop 0.0 1.0 0
7957693 rs3 53447.0 7.0 Windows.Desktop 0.0 1.0 0
8564928 rs4 53447.0 7.0 Windows.Desktop 0.0 1.0 1
In [109]:
df[categorical_columns].describe().T
Out[109]:
count mean std min 25% 50% 75% max
CityIdentifier 8920724.0 80639.789062 48838.632812 5.0 36825.0 82373.0 124736.0 167962.0
In [110]:
df_test.AvSigVersion.nunique()
Out[110]:
9357
In [111]:
df.AvSigVersion.nunique()
Out[111]:
8531
In [112]:
len(set(df_test.AvSigVersion.unique())-set(df.AvSigVersion.unique()))
Out[112]:
1092
In [113]:
fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['HasDetections'],hue=df['OsPlatformSubRelease'],ax=ax)
Out[113]:
<matplotlib.axes._subplots.AxesSubplot at 0x1cf08da03c8>
In [181]:
fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['OsPlatformSubRelease'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[181]:
<matplotlib.axes._subplots.AxesSubplot at 0x24069fd35f8>
In [182]:
fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['Platform'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[182]:
<matplotlib.axes._subplots.AxesSubplot at 0x23fb17eee10>
In [183]:
fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['Census_ProcessorManufacturerIdentifier'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[183]:
<matplotlib.axes._subplots.AxesSubplot at 0x240c3c657f0>
In [184]:
fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['RtpStateBitfield'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[184]:
<matplotlib.axes._subplots.AxesSubplot at 0x24105bcceb8>
In [185]:
fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['IsBeta'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[185]:
<matplotlib.axes._subplots.AxesSubplot at 0x2405aabe7b8>
In [186]:
fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['AVProductsInstalled'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[186]:
<matplotlib.axes._subplots.AxesSubplot at 0x240ff48e048>
In [187]:
fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['AVProductsEnabled'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[187]:
<matplotlib.axes._subplots.AxesSubplot at 0x240a73d8c18>
In [188]:
fig,ax=plt.subplots(figsize=(12,7))
sns.countplot(x=df['SkuEdition'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[188]:
<matplotlib.axes._subplots.AxesSubplot at 0x24126963518>
In [189]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df['SmartScreen'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[189]:
<matplotlib.axes._subplots.AxesSubplot at 0x23f0e29ecf8>
In [190]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df['Census_MDC2FormFactor'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[190]:
<matplotlib.axes._subplots.AxesSubplot at 0x2406ee1cdd8>
In [191]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df['Wdft_IsGamer'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[191]:
<matplotlib.axes._subplots.AxesSubplot at 0x2413092aef0>
In [192]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df['AVProductsInstalled'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[192]:
<matplotlib.axes._subplots.AxesSubplot at 0x240d22a7518>
In [193]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[df.Wdft_IsGamer==1]['AVProductsInstalled'],hue=df[df.Wdft_IsGamer==1]['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[193]:
<matplotlib.axes._subplots.AxesSubplot at 0x23ef9ffd0f0>
In [194]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[df.Wdft_IsGamer==1]['AVProductsEnabled'],hue=df[df.Wdft_IsGamer==1]['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[194]:
<matplotlib.axes._subplots.AxesSubplot at 0x2405cfbf160>
In [195]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[df.EngineVersion.isin(['1.1.15200.1','1.1.15100.1'])]['AVProductsEnabled'],hue=df[df.EngineVersion.isin(['1.1.15200.1','1.1.15100.1'])]['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[195]:
<matplotlib.axes._subplots.AxesSubplot at 0x2412d318a58>
In [196]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[df.EngineVersion.isin(['1.1.15200.1','1.1.15100.1'])]['Wdft_IsGamer'],hue=df[df.EngineVersion.isin(['1.1.15200.1','1.1.15100.1'])]['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[196]:
<matplotlib.axes._subplots.AxesSubplot at 0x2409768ea90>
In [197]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[df.AppVersion=='4.18.1807.18075']['Wdft_IsGamer'],hue=df[df.AppVersion=='4.18.1807.18075']['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[197]:
<matplotlib.axes._subplots.AxesSubplot at 0x23ef26f2278>
In [198]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df['Wdft_IsGamer'],hue=df['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[198]:
<matplotlib.axes._subplots.AxesSubplot at 0x24093c82cc0>
In [121]:
fig,ax=plt.subplots(figsize=(20,7))
sns.countplot(x=df[(df.AVProductsInstalled==1)&(df.Wdft_IsGamer==1)]['ProductName'],hue=df[(df.AVProductsInstalled==1)&(df.Wdft_IsGamer==1)]['HasDetections'],ax=ax,palette={0:'g',1:'r'})
Out[121]:
<matplotlib.axes._subplots.AxesSubplot at 0x1fbecdc0390>
In [119]:
#df['AVProductsInstalled_1']=[1 if x==1 else 0 for x in df['AVProductsInstalled']]

#df['Interaction_17']=np.logical_and(np.logical_and(df.AVProductsInstalled_1,df.Wdft_IsGamer),df.ProductName_win8defender.astype('bool')).astype('int8')
In [203]:
my_plots(df,'OsBuild')
In [204]:
my_plots(df,categorical_columns[1])
In [205]:
my_plots(df,categorical_columns[2])
In [206]:
my_plots(df,categorical_columns[3])
In [207]:
my_plots(df,categorical_columns[4])
In [208]:
my_plots(df,categorical_columns[5])
In [209]:
my_plots(df,categorical_columns[6])
In [210]:
my_plots(df,categorical_columns[7])
In [211]:
my_plots(df,categorical_columns[8])
In [212]:
my_plots(df,categorical_columns[9])
In [213]:
my_plots(df,categorical_columns[10])
In [214]:
my_plots(df,categorical_columns[11])
In [215]:
my_plots(df,categorical_columns[12])
In [216]:
my_plots(df,categorical_columns[13])
In [217]:
my_plots(df,categorical_columns[14])
In [218]:
my_plots(df,categorical_columns[15])
In [219]:
my_plots(df,categorical_columns[16])
In [220]:
my_plots(df,categorical_columns[17])
In [221]:
my_plots(df,categorical_columns[18])
In [222]:
my_plots(df,categorical_columns[19])
In [223]:
my_plots(df,categorical_columns[21])
In [224]:
my_plots(df,categorical_columns[22])
In [225]:
my_plots(df,categorical_columns[23])
In [226]:
my_plots(df,categorical_columns[24])
In [227]:
my_plots(df,categorical_columns[25])
In [228]:
my_plots(df,categorical_columns[26])
In [229]:
my_plots(df,categorical_columns[27])
In [230]:
my_plots(df,categorical_columns[28])
In [231]:
my_plots(df,categorical_columns[29])
In [232]:
my_plots(df,categorical_columns[30])
In [233]:
my_plots(df,categorical_columns[31])
In [234]:
my_plots(df,categorical_columns[32])
In [235]:
my_plots(df,categorical_columns[33])
In [236]:
my_plots(df,categorical_columns[34])
In [237]:
my_plots(df,categorical_columns[35])
In [238]:
my_plots(df,categorical_columns[36])
In [239]:
my_plots(df,categorical_columns[37])
In [240]:
my_plots(df,categorical_columns[38])
In [241]:
my_plots(df,categorical_columns[39])
In [242]:
my_plots(df,categorical_columns[40])
In [243]:
my_plots(df,categorical_columns[41])
In [244]:
my_plots(df,categorical_columns[42])
In [245]:
my_plots(df,categorical_columns[43])
In [246]:
my_plots(df,categorical_columns[44])
In [247]:
my_plots(df,categorical_columns[45])
In [248]:
my_plots(df,categorical_columns[46])
In [249]:
my_plots(df,categorical_columns[47])

Frequency Encoding

In [81]:
import category_encoders as ce
In [83]:
from tqdm import tqdm
import warnings
import gc
import time
from sklearn.metrics import mean_squared_error
warnings.simplefilter(action='ignore', category=FutureWarning)
def frequency_encoding(df,df_test,variable):
    t = pd.concat([df[variable], df_test[variable]]).value_counts().reset_index()
    t = t.reset_index()
    t.loc[t[variable] == 1, 'level_0'] = np.nan
    t.set_index('index', inplace=True)
    max_label = t['level_0'].max() + 1
    t.fillna(max_label, inplace=True)
    return t.to_dict()['level_0']
In [84]:
%%time
for variable in tqdm(categorical_columns):
    freq_enc_dict = frequency_encoding(df,df_test,variable)
    df[variable] = df[variable].map(lambda x: freq_enc_dict.get(x, np.nan))
    df_test[variable] = df_test[variable].map(lambda x: freq_enc_dict.get(x, np.nan))
    categorical_columns.remove(variable)
 50%|█████████████████████████████████████████                                         | 24/48 [00:43<00:57,  2.38s/it]
Wall time: 43.9 s
In [85]:
%%time
indexer = {}
for col in tqdm(categorical_columns):
    if col == 'MachineIdentifier': continue
    _, indexer[col] = pd.factorize(df[col])
    
for col in tqdm(categorical_columns):
    if col == 'MachineIdentifier': continue
    df[col] = indexer[col].get_indexer(df[col])
    df_test[col] = indexer[col].get_indexer(df_test[col])
100%|██████████████████████████████████████████████████████████████████████████████████| 24/24 [00:09<00:00,  3.53it/s]
100%|██████████████████████████████████████████████████████████████████████████████████| 24/24 [36:39<00:00, 67.87s/it]
Wall time: 36min 48s
In [86]:
%%time
for col in df.columns[1:]:
    if df[col].nunique()==2:
        print(df[col].value_counts())
        print("\n")
0    8920657
1         67
Name: IsBeta, dtype: int64


0    8766103
1     154621
Name: IsSxsPassiveMode, dtype: int64


1    8813414
0     107310
Name: HasTpm, dtype: int64


1.0    8437593
0.0     483131
Name: IsProtected, dtype: int64


0    8920466
1        258
Name: AutoSampleOptIn, dtype: int64


0.0    8916843
1.0       3881
Name: SMode, dtype: int64


1.0    8731622
0.0     189102
Name: Firewall, dtype: int64


0    8232180
1     688544
Name: Census_HasOpticalDiskDrive, dtype: int64


0    8915860
1       4864
Name: Census_IsPortableOperatingSystem, dtype: int64


0.0    8920637
1.0         87
Name: Census_IsFlightsDisabled, dtype: int64


0.0    8919908
1.0        816
Name: Census_ThresholdOptIn, dtype: int64


0    4584979
1    4335745
Name: Census_IsSecureBootEnabled, dtype: int64


0.0    8920723
1.0          1
Name: Census_IsWIMBootEnabled, dtype: int64


0.0    8858046
1.0      62678
Name: Census_IsVirtualDevice, dtype: int64


0    7800821
1    1119903
Name: Census_IsTouchEnabled, dtype: int64


0    8581166
1     339558
Name: Census_IsPenCapable, dtype: int64


0.0    8412643
1.0     508081
Name: Census_IsAlwaysOnAlwaysConnectedCapable, dtype: int64


0.0    6477019
1.0    2443705
Name: Wdft_IsGamer, dtype: int64


0    4462256
1    4458468
Name: HasDetections, dtype: int64


0    8899401
1      21323
Name: Month_1, dtype: int64


0    8872556
1      48168
Name: Month_2, dtype: int64


0    7826917
1    1093807
Name: Month_3, dtype: int64


0    4897093
1    4023631
Name: Month_4, dtype: int64


0    7629648
1    1291076
Name: Month_5, dtype: int64


0    8580966
1     339758
Name: Month_6, dtype: int64


0    8697782
1     222942
Name: Month_7, dtype: int64


0    8766232
1     154492
Name: Month_8, dtype: int64


0    7441694
1    1479030
Name: Month_9, dtype: int64


0    8821495
1      99229
Name: Month_10, dtype: int64


0    8851301
1      69423
Name: Month_11, dtype: int64


0    8842879
1      77845
Name: Month_12, dtype: int64


0    8920567
1        157
Name: Year_9, dtype: int64


0    8920543
1        181
Name: Year_10, dtype: int64


0    8920430
1        294
Name: Year_11, dtype: int64


0    8920684
1         40
Name: Year_12, dtype: int64


0    8920458
1        266
Name: Year_13, dtype: int64


0    8919032
1       1692
Name: Year_14, dtype: int64


0    8829890
1      90834
Name: Year_15, dtype: int64


0    8599707
1     321017
Name: Year_16, dtype: int64


0    6249001
1    2671723
Name: Year_17, dtype: int64


1    5834520
0    3086204
Name: Year_18, dtype: int64


0    8920569
1        155
Name: Interaction_MYYear_9Month_7, dtype: int64


0    8920722
1          2
Name: Interaction_MYYear_9Month_12, dtype: int64


0    8920712
1         12
Name: Interaction_MYYear_10Month_2, dtype: int64


0    8920714
1         10
Name: Interaction_MYYear_10Month_6, dtype: int64


0    8920723
1          1
Name: Interaction_MYYear_10Month_9, dtype: int64


0    8920716
1          8
Name: Interaction_MYYear_10Month_10, dtype: int64


0    8920574
1        150
Name: Interaction_MYYear_10Month_11, dtype: int64


0    8920477
1        247
Name: Interaction_MYYear_11Month_4, dtype: int64


0    8920688
1         36
Name: Interaction_MYYear_11Month_6, dtype: int64


0    8920721
1          3
Name: Interaction_MYYear_11Month_10, dtype: int64


0    8920716
1          8
Name: Interaction_MYYear_11Month_11, dtype: int64


0    8920703
1         21
Name: Interaction_MYYear_12Month_3, dtype: int64


0    8920720
1          4
Name: Interaction_MYYear_12Month_4, dtype: int64


0    8920720
1          4
Name: Interaction_MYYear_12Month_5, dtype: int64


0    8920713
1         11
Name: Interaction_MYYear_12Month_8, dtype: int64


0    8920719
1          5
Name: Interaction_MYYear_13Month_1, dtype: int64


0    8920671
1         53
Name: Interaction_MYYear_13Month_3, dtype: int64


0    8920721
1          3
Name: Interaction_MYYear_13Month_5, dtype: int64


0    8920720
1          4
Name: Interaction_MYYear_13Month_7, dtype: int64


0    8920539
1        185
Name: Interaction_MYYear_13Month_8, dtype: int64


0    8920723
1          1
Name: Interaction_MYYear_13Month_9, dtype: int64


0    8920709
1         15
Name: Interaction_MYYear_13Month_10, dtype: int64


0    8920704
1         20
Name: Interaction_MYYear_14Month_2, dtype: int64


0    8920075
1        649
Name: Interaction_MYYear_14Month_3, dtype: int64


0    8920381
1        343
Name: Interaction_MYYear_14Month_7, dtype: int64


0    8920615
1        109
Name: Interaction_MYYear_14Month_8, dtype: int64


0    8920179
1        545
Name: Interaction_MYYear_14Month_10, dtype: int64


0    8920698
1         26
Name: Interaction_MYYear_14Month_12, dtype: int64


0    8920509
1        215
Name: Interaction_MYYear_15Month_1, dtype: int64


0    8920691
1         33
Name: Interaction_MYYear_15Month_2, dtype: int64


0    8920168
1        556
Name: Interaction_MYYear_15Month_3, dtype: int64


0    8920688
1         36
Name: Interaction_MYYear_15Month_4, dtype: int64


0    8920573
1        151
Name: Interaction_MYYear_15Month_5, dtype: int64


0    8885886
1      34838
Name: Interaction_MYYear_15Month_7, dtype: int64


0    8914201
1       6523
Name: Interaction_MYYear_15Month_8, dtype: int64


0    8919121
1       1603
Name: Interaction_MYYear_15Month_9, dtype: int64


0    8888974
1      31750
Name: Interaction_MYYear_15Month_10, dtype: int64


0    8905750
1      14974
Name: Interaction_MYYear_15Month_11, dtype: int64


0    8920569
1        155
Name: Interaction_MYYear_15Month_12, dtype: int64


0    8906060
1      14664
Name: Interaction_MYYear_16Month_1, dtype: int64


0    8895566
1      25158
Name: Interaction_MYYear_16Month_2, dtype: int64


0    8912207
1       8517
Name: Interaction_MYYear_16Month_3, dtype: int64


0    8906477
1      14247
Name: Interaction_MYYear_16Month_4, dtype: int64


0    8907956
1      12768
Name: Interaction_MYYear_16Month_5, dtype: int64


0    8905557
1      15167
Name: Interaction_MYYear_16Month_6, dtype: int64


0    8857429
1      63295
Name: Interaction_MYYear_16Month_7, dtype: int64


0    8902084
1      18640
Name: Interaction_MYYear_16Month_8, dtype: int64


0    8898163
1      22561
Name: Interaction_MYYear_16Month_9, dtype: int64


0    8870372
1      50352
Name: Interaction_MYYear_16Month_10, dtype: int64


0    8895744
1      24980
Name: Interaction_MYYear_16Month_11, dtype: int64


0    8870056
1      50668
Name: Interaction_MYYear_16Month_12, dtype: int64


0    8919999
1        725
Name: Interaction_MYYear_17Month_1, dtype: int64


0    8919326
1       1398
Name: Interaction_MYYear_17Month_2, dtype: int64


0    8096779
1     823945
Name: Interaction_MYYear_17Month_3, dtype: int64


0    8891286
1      29438
Name: Interaction_MYYear_17Month_4, dtype: int64


0    8919438
1       1286
Name: Interaction_MYYear_17Month_5, dtype: int64


0    8690223
1     230501
Name: Interaction_MYYear_17Month_6, dtype: int64


0    8860746
1      59978
Name: Interaction_MYYear_17Month_7, dtype: int64


0    8919186
1       1538
Name: Interaction_MYYear_17Month_8, dtype: int64


0    7470671
1    1450053
Name: Interaction_MYYear_17Month_9, dtype: int64


0    8904168
1      16556
Name: Interaction_MYYear_17Month_10, dtype: int64


0    8891413
1      29311
Name: Interaction_MYYear_17Month_11, dtype: int64


0    8893730
1      26994
Name: Interaction_MYYear_17Month_12, dtype: int64


0    8915010
1       5714
Name: Interaction_MYYear_18Month_1, dtype: int64


0    8899177
1      21547
Name: Interaction_MYYear_18Month_2, dtype: int64


0    8660658
1     260066
Name: Interaction_MYYear_18Month_3, dtype: int64


0    4941065
1    3979659
Name: Interaction_MYYear_18Month_4, dtype: int64


0    7643860
1    1276864
Name: Interaction_MYYear_18Month_5, dtype: int64


0    8826680
1      94044
Name: Interaction_MYYear_18Month_6, dtype: int64


0    8856395
1      64329
Name: Interaction_MYYear_18Month_7, dtype: int64


0    8793238
1     127486
Name: Interaction_MYYear_18Month_8, dtype: int64


0    8915913
1       4811
Name: Interaction_MYYear_18Month_9, dtype: int64


0    7493205
1    1427519
Name: Device_PossibleOwnership, dtype: int64


Wall time: 41.7 s
In [87]:
df.drop(columns=['Interaction_MYYear_15Month_12','Interaction_MYYear_15Month_5','Interaction_MYYear_15Month_4','Interaction_MYYear_15Month_2','Interaction_MYYear_14Month_12','Interaction_MYYear_14Month_8','Interaction_MYYear_14Month_2','Interaction_MYYear_13Month_10','Interaction_MYYear_13Month_9','Interaction_MYYear_13Month_8','Interaction_MYYear_13Month_7','Interaction_MYYear_13Month_5','Interaction_MYYear_13Month_3','Interaction_MYYear_13Month_1','Interaction_MYYear_12Month_8','Interaction_MYYear_12Month_5','Interaction_MYYear_12Month_4','Interaction_MYYear_12Month_3','Interaction_MYYear_11Month_11','Interaction_MYYear_11Month_10','Interaction_MYYear_11Month_6','Interaction_MYYear_11Month_4','Interaction_MYYear_10Month_11','Interaction_MYYear_10Month_10','Interaction_MYYear_10Month_9','Interaction_MYYear_10Month_6','Year_9','Year_10','Year_11','Year_12','Year_13','Interaction_MYYear_9Month_7','Interaction_MYYear_9Month_12','Interaction_MYYear_10Month_2'],inplace=True)
df_test.drop(columns=['Interaction_MYYear_15Month_12','Interaction_MYYear_15Month_5','Interaction_MYYear_15Month_4','Interaction_MYYear_15Month_2','Interaction_MYYear_14Month_12','Interaction_MYYear_14Month_8','Interaction_MYYear_14Month_2','Interaction_MYYear_13Month_10','Interaction_MYYear_13Month_9','Interaction_MYYear_13Month_8','Interaction_MYYear_13Month_7','Interaction_MYYear_13Month_5','Interaction_MYYear_13Month_3','Interaction_MYYear_13Month_1','Interaction_MYYear_12Month_8','Interaction_MYYear_12Month_5','Interaction_MYYear_12Month_4','Interaction_MYYear_12Month_3','Interaction_MYYear_11Month_11','Interaction_MYYear_11Month_10','Interaction_MYYear_11Month_6','Interaction_MYYear_11Month_4','Interaction_MYYear_10Month_11','Interaction_MYYear_10Month_10','Interaction_MYYear_10Month_9','Interaction_MYYear_10Month_6','Year_9','Year_10','Year_11','Year_12','Year_13','Interaction_MYYear_9Month_7','Interaction_MYYear_9Month_12','Interaction_MYYear_10Month_2'],inplace=True)
In [88]:
df.shape
Out[88]:
(8920724, 187)
In [89]:
df_test.shape
Out[89]:
(7853253, 186)
In [90]:
%%time
#df.drop(columns=list(df.columns[df.columns.str.endswith('encode')]),inplace=True)
#df_test.drop(columns=list(df_test.columns[df_test.columns.str.endswith('encode')]),inplace=True)
Wall time: 0 ns
In [91]:
%%time
df=reduce_memory(df)
df_test=reduce_memory(df_test)
Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 1793065524
Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 1625623371
Wall time: 53.1 s
In [92]:
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 8920724 entries, 0 to 8921482
Columns: 187 entries, MachineIdentifier to Device_PossibleOwnership
dtypes: category(17), float16(23), float32(4), int16(9), int32(2), int8(132)
memory usage: 2.5 GB
In [93]:
df_test.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7853253 entries, 0 to 7853252
Columns: 186 entries, MachineIdentifier to Device_PossibleOwnership
dtypes: category(16), float16(24), float32(4), int16(9), int32(2), int8(131)
memory usage: 2.1 GB
In [94]:
%%time
s=df['AVProductsInstalled']-df['AVProductsEnabled']
r=df_test['AVProductsInstalled']-df_test['AVProductsEnabled']

df['AV_highrisk']=[1 if (x==0)&(y==0) else 0 for x,y in zip(df.AVProductsInstalled,s)]
df_test['AV_highrisk']=[1 if (x==0)&(y==0) else 0 for x,y in zip(df_test.AVProductsInstalled,r)]



df['AV_mediumrisk']=[1 if (x==1)&(y==0) else 0 for x,y in zip(df.AVProductsInstalled,s)]
df_test['AV_mediumrisk']=[1 if (x==1)&(y==0) else 0 for x,y in zip(df_test.AVProductsInstalled,r)]

df['AV_lowrisk']=[1 if (x>1)&(y>1) else 0 for x,y in zip(df.AVProductsInstalled,s)]
df_test['AV_lowrisk']=[1 if (x==0)&(y==0) else 0 for x,y in zip(df_test.AVProductsInstalled,r)]
Wall time: 22.5 s
In [95]:
df.columns[df.isnull().any()]
Out[95]:
Index([], dtype='object')
In [96]:
df.shape
Out[96]:
(8920724, 190)
In [97]:
df_test.shape
Out[97]:
(7853253, 189)
In [98]:
df.memory_usage(deep=True).sum()
Out[98]:
3573865091
In [99]:
df_test.memory_usage(deep=True).sum()
Out[99]:
3115615234
In [100]:
df[numerical_columns].corr()
Out[100]:
Census_ProcessorCoreCount Census_PrimaryDiskTotalCapacity Census_SystemVolumeTotalCapacity Census_TotalPhysicalRAM Census_InternalPrimaryDiagonalDisplaySizeInInches Census_InternalPrimaryDisplayResolutionHorizontal Census_InternalPrimaryDisplayResolutionVertical Census_InternalBatteryNumberOfCharges
Census_ProcessorCoreCount 1.000000 -0.000187 0.081713 0.595529 0.183298 0.344943 0.315479 0.117078
Census_PrimaryDiskTotalCapacity -0.000187 1.000000 0.000787 -0.000066 -0.000059 -0.000178 -0.000250 -0.000334
Census_SystemVolumeTotalCapacity 0.081713 0.000787 1.000000 0.153180 0.081792 0.019730 -0.033649 -0.001289
Census_TotalPhysicalRAM 0.595529 -0.000066 0.153180 1.000000 0.241174 0.366244 0.336088 0.182585
Census_InternalPrimaryDiagonalDisplaySizeInInches 0.183298 -0.000059 0.081792 0.241174 1.000000 0.320344 0.277455 0.513360
Census_InternalPrimaryDisplayResolutionHorizontal 0.344943 -0.000178 0.019730 0.366244 0.320344 1.000000 0.901674 0.169044
Census_InternalPrimaryDisplayResolutionVertical 0.315479 -0.000250 -0.033649 0.336088 0.277455 0.901674 1.000000 0.230196
Census_InternalBatteryNumberOfCharges 0.117078 -0.000334 -0.001289 0.182585 0.513360 0.169044 0.230196 1.000000
In [101]:
max(df.Census_InternalPrimaryDisplayResolutionVertical)
Out[101]:
8640.0
In [102]:
max(df.Census_InternalPrimaryDisplayResolutionHorizontal)
Out[102]:
12288.0
In [103]:
df[df.Census_InternalPrimaryDisplayResolutionHorizontal==12288.0][numerical_columns]
Out[103]:
Census_ProcessorCoreCount Census_PrimaryDiskTotalCapacity Census_SystemVolumeTotalCapacity Census_TotalPhysicalRAM Census_InternalPrimaryDiagonalDisplaySizeInInches Census_InternalPrimaryDisplayResolutionHorizontal Census_InternalPrimaryDisplayResolutionVertical Census_InternalBatteryNumberOfCharges
1072023 8.0 238475.0 237428.0 32768.0 54.59375 12288.0 2160.0 4.294967e+09
1252344 12.0 476940.0 476389.0 16384.0 46.00000 12288.0 2160.0 4.294967e+09
3152519 4.0 114473.0 113661.0 16384.0 47.40625 12288.0 2160.0 4.294967e+09
In [104]:
display_horizontal=np.median(df.Census_InternalPrimaryDisplayResolutionHorizontal)
In [105]:
display_vertical=np.median(df.Census_InternalPrimaryDisplayResolutionVertical)
In [106]:
df['Census_InternalPrimaryDisplayResolutionHorizontal']=[display_horizontal if x>=10000.0 else x for x in df['Census_InternalPrimaryDisplayResolutionHorizontal']]
df['Census_InternalPrimaryDisplayResolutionVertical']=[display_vertical if x>=6000 else x for x in df['Census_InternalPrimaryDisplayResolutionVertical']]
df_test['Census_InternalPrimaryDisplayResolutionHorizontal']=[display_horizontal if x>=10000.0 else x for x in df_test['Census_InternalPrimaryDisplayResolutionHorizontal']]
df_test['Census_InternalPrimaryDisplayResolutionVertical']=[display_vertical if x>=6000 else x for x in df_test['Census_InternalPrimaryDisplayResolutionVertical']]
In [107]:
df['Interaction_01']=df['Census_TotalPhysicalRAM']/df['Census_ProcessorCoreCount']
df_test['Interaction_01']=df_test['Census_TotalPhysicalRAM']/df_test['Census_ProcessorCoreCount']
df['Interaction_02']=abs(df.Census_PrimaryDiskTotalCapacity- df.Census_SystemVolumeTotalCapacity)
df_test['Interaction_02']=abs(df_test.Census_PrimaryDiskTotalCapacity- df_test.Census_SystemVolumeTotalCapacity)
df['Interaction_03']=np.multiply(df.Census_InternalPrimaryDisplayResolutionHorizontal,df.Census_InternalPrimaryDisplayResolutionVertical)
df_test['Interaction_03']=np.multiply(df_test.Census_InternalPrimaryDisplayResolutionHorizontal,df_test.Census_InternalPrimaryDisplayResolutionVertical)
In [108]:
df=reduce_memory(df)
Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 330066788
In [109]:
df_test=reduce_memory(df_test)
Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 290570361
In [110]:
np.min(df.Census_InternalPrimaryDisplayResolutionHorizontal)
Out[110]:
-1.0
In [111]:
np.min(df.Census_InternalPrimaryDisplayResolutionVertical)
Out[111]:
-1.0
In [112]:
df[df.Census_InternalPrimaryDisplayResolutionHorizontal==-1][numerical_columns]
Out[112]:
Census_ProcessorCoreCount Census_PrimaryDiskTotalCapacity Census_SystemVolumeTotalCapacity Census_TotalPhysicalRAM Census_InternalPrimaryDiagonalDisplaySizeInInches Census_InternalPrimaryDisplayResolutionHorizontal Census_InternalPrimaryDisplayResolutionVertical Census_InternalBatteryNumberOfCharges
86641 4.0 953869.0 911955.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
174431 4.0 953869.0 910716.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
245597 4.0 953869.0 912060.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
340295 2.0 476940.0 456362.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
403759 4.0 476940.0 51200.0 2048.0 15.500000 -1.0 -1.0 0.000000e+00
439181 4.0 953869.0 912402.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
540172 8.0 953869.0 939567.0 8192.0 15.500000 -1.0 -1.0 4.294967e+09
627565 6.0 953869.0 99019.0 8192.0 15.500000 -1.0 -1.0 4.294967e+09
695468 4.0 953869.0 911197.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
1003210 4.0 953869.0 911997.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
1133025 4.0 476940.0 433904.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
1138140 4.0 29600.0 28920.0 1024.0 15.500000 -1.0 -1.0 1.400000e+01
1170739 4.0 476940.0 435167.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
1173099 4.0 476940.0 433682.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
1190003 8.0 953869.0 912728.0 8192.0 15.500000 -1.0 -1.0 0.000000e+00
1240120 2.0 152627.0 101949.0 3072.0 15.500000 -1.0 -1.0 4.294967e+09
1259038 4.0 953869.0 910677.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
1301984 4.0 122104.0 106620.0 4096.0 11.601562 -1.0 -1.0 0.000000e+00
1304544 2.0 476940.0 466938.0 2048.0 15.500000 -1.0 -1.0 4.294967e+09
1349133 4.0 244198.0 102401.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
1411395 4.0 29600.0 28482.0 2048.0 15.500000 -1.0 -1.0 1.900000e+01
1545770 2.0 122104.0 105636.0 8192.0 15.500000 -1.0 -1.0 0.000000e+00
1578287 4.0 122104.0 120963.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
1648906 4.0 122104.0 97721.0 8192.0 15.500000 -1.0 -1.0 0.000000e+00
1654681 4.0 953869.0 461862.0 6144.0 13.898438 -1.0 -1.0 0.000000e+00
1792269 8.0 114473.0 113907.0 16384.0 15.500000 -1.0 -1.0 4.294967e+09
1797716 8.0 122104.0 61440.0 8192.0 15.500000 -1.0 -1.0 0.000000e+00
1814557 4.0 476940.0 460857.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
1835472 4.0 476940.0 435687.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
1838169 4.0 953869.0 476159.0 8192.0 15.500000 -1.0 -1.0 4.294967e+09
... ... ... ... ... ... ... ... ...
7009604 4.0 953869.0 907644.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
7058425 2.0 476940.0 435799.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
7103069 2.0 305245.0 267829.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
7143810 4.0 953869.0 913225.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
7174106 2.0 953869.0 81921.0 4096.0 15.500000 -1.0 -1.0 4.294967e+09
7311245 2.0 476940.0 462394.0 4096.0 15.500000 -1.0 -1.0 8.300000e+01
7509075 2.0 305245.0 104448.0 2048.0 15.500000 -1.0 -1.0 4.294967e+09
7598414 4.0 122104.0 120817.0 4096.0 15.500000 -1.0 -1.0 2.800000e+01
7637536 2.0 305245.0 304693.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
7801589 4.0 228936.0 123000.0 8192.0 15.500000 -1.0 -1.0 4.294967e+09
7841312 2.0 476940.0 460562.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
7841507 4.0 953869.0 184207.0 8192.0 15.500000 -1.0 -1.0 4.294967e+09
7926866 4.0 715404.0 691297.0 6144.0 15.500000 -1.0 -1.0 0.000000e+00
7976558 4.0 953869.0 380358.0 4096.0 15.500000 -1.0 -1.0 1.250000e+02
8038076 4.0 953869.0 912986.0 6144.0 15.500000 -1.0 -1.0 0.000000e+00
8100681 2.0 305245.0 289783.0 2048.0 15.500000 -1.0 -1.0 0.000000e+00
8187745 2.0 476940.0 461258.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
8247734 6.0 953869.0 249025.0 8192.0 15.500000 -1.0 -1.0 4.294967e+09
8273003 8.0 122104.0 121027.0 4096.0 15.500000 -1.0 -1.0 3.300000e+01
8380921 2.0 305245.0 99495.0 2048.0 15.500000 -1.0 -1.0 4.294967e+09
8381285 4.0 29600.0 28920.0 1024.0 15.500000 -1.0 -1.0 9.000000e+00
8430613 2.0 152587.0 151614.0 2048.0 15.500000 -1.0 -1.0 4.294967e+09
8463891 2.0 953869.0 285672.0 4096.0 15.500000 -1.0 -1.0 4.294967e+09
8476044 4.0 953869.0 234246.0 8192.0 15.500000 -1.0 -1.0 0.000000e+00
8528644 2.0 114473.0 113920.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
8563916 4.0 953869.0 910611.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
8653632 4.0 476940.0 435447.0 4096.0 23.000000 -1.0 -1.0 0.000000e+00
8694644 4.0 953869.0 912461.0 4096.0 15.500000 -1.0 -1.0 0.000000e+00
8741691 4.0 22902.0 22335.0 6144.0 15.500000 -1.0 -1.0 0.000000e+00
8759520 2.0 114473.0 113500.0 4096.0 15.500000 -1.0 -1.0 4.294967e+09

156 rows × 8 columns

In [113]:
df['Census_InternalPrimaryDisplayResolutionHorizontal']=[display_horizontal if x==-1 else x for x in df['Census_InternalPrimaryDisplayResolutionHorizontal']]
df['Census_InternalPrimaryDisplayResolutionVertical']=[display_vertical if x==-1 else x for x in df['Census_InternalPrimaryDisplayResolutionVertical']]
df_test['Census_InternalPrimaryDisplayResolutionHorizontal']=[display_horizontal if x==-1 else x for x in df_test['Census_InternalPrimaryDisplayResolutionHorizontal']]
df_test['Census_InternalPrimaryDisplayResolutionVertical']=[display_vertical if x==-1 else x for x in df_test['Census_InternalPrimaryDisplayResolutionVertical']]
In [114]:
df.Census_InternalBatteryNumberOfCharges.describe()
Out[114]:
count    8.920724e+06
mean     1.089876e+09
std      1.925254e+09
min      0.000000e+00
25%      0.000000e+00
50%      0.000000e+00
75%      4.294967e+09
max      4.294967e+09
Name: Census_InternalBatteryNumberOfCharges, dtype: float64
In [115]:
df.Census_InternalBatteryNumberOfCharges.value_counts().head()
Out[115]:
0.000000e+00    5321817
4.294967e+09    2263693
1.000000e+00      53810
2.000000e+00      28128
1.600000e+01      27348
Name: Census_InternalBatteryNumberOfCharges, dtype: int64
In [116]:
df['PPI']=np.sqrt(np.power(df.Census_InternalPrimaryDisplayResolutionHorizontal,2)+np.power(df.Census_InternalPrimaryDisplayResolutionVertical,2))/df.Census_InternalPrimaryDiagonalDisplaySizeInInches
In [117]:
df_test['PPI']=np.sqrt(np.power(df_test.Census_InternalPrimaryDisplayResolutionHorizontal,2)+np.power(df_test.Census_InternalPrimaryDisplayResolutionVertical,2))/df_test.Census_InternalPrimaryDiagonalDisplaySizeInInches
In [119]:
df=reduce_memory(df)
Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 160573032
In [120]:
df_test=reduce_memory(df_test)
Changing Datatypes please wait.......
Datatypes updated and memory usage is reduced by : 141358554
In [121]:
print("Memory Usage of training set in Bytes : ",df.memory_usage(deep=True).sum())
print("Memory Usage of test set in Bytes : ",df_test.memory_usage(deep=True).sum())
Memory Usage of training set in Bytes :  3511420023
Memory Usage of test set in Bytes :  3060642463
In [122]:
set(df_test.EngineVersion.unique())-set(df.EngineVersion.unique())
Out[122]:
{-1}
In [123]:
%%time
different_columns=[]
for i in range(1,len(df_test.columns)):
    if df.columns[i] in ['MachineIdentifier','HasDetections']:
        continue
    elif len(set(df_test[df.columns[i]].unique())-set(df[df.columns[i]].unique()))==0:
        print(df.columns[i])
        continue
    else:
        different_columns.append(df.columns[i])
ProductName
IsBeta
IsSxsPassiveMode
AVProductsInstalled
AVProductsEnabled
HasTpm
CountryIdentifier
LocaleEnglishNameIdentifier
Platform
Processor
OsSuite
OsPlatformSubRelease
SkuEdition
IsProtected
AutoSampleOptIn
SMode
Firewall
Census_DeviceFamily
Census_ProcessorManufacturerIdentifier
Census_PrimaryDiskTypeName
Census_HasOpticalDiskDrive
Census_PowerPlatformRoleName
Census_OSArchitecture
Census_OSInstallTypeName
Census_OSInstallLanguageIdentifier
Census_OSWUAutoUpdateOptionsName
Census_IsPortableOperatingSystem
Census_GenuineStateName
Census_ActivationChannel
Census_IsFlightsDisabled
Census_ThresholdOptIn
Census_IsSecureBootEnabled
Census_IsWIMBootEnabled
Census_IsVirtualDevice
Census_IsTouchEnabled
Census_IsPenCapable
Census_IsAlwaysOnAlwaysConnectedCapable
Wdft_IsGamer
Wdft_RegionIdentifier
Month_1
Month_2
Month_3
Month_4
Month_5
Month_6
Month_7
Month_8
Month_9
Month_10
Month_11
Month_12
Year_14
Year_15
Year_16
Year_17
Year_18
Interaction_MYYear_9Month_1
Interaction_MYYear_9Month_2
Interaction_MYYear_9Month_3
Interaction_MYYear_9Month_4
Interaction_MYYear_9Month_5
Interaction_MYYear_9Month_6
Interaction_MYYear_9Month_9
Interaction_MYYear_9Month_10
Interaction_MYYear_9Month_11
Interaction_MYYear_10Month_1
Interaction_MYYear_10Month_3
Interaction_MYYear_10Month_4
Interaction_MYYear_10Month_5
Interaction_MYYear_10Month_7
Interaction_MYYear_10Month_8
Interaction_MYYear_10Month_12
Interaction_MYYear_11Month_1
Interaction_MYYear_11Month_2
Interaction_MYYear_11Month_3
Interaction_MYYear_11Month_5
Interaction_MYYear_11Month_7
Interaction_MYYear_11Month_8
Interaction_MYYear_11Month_9
Interaction_MYYear_11Month_12
Interaction_MYYear_12Month_1
Interaction_MYYear_12Month_6
Interaction_MYYear_12Month_7
Interaction_MYYear_12Month_9
Interaction_MYYear_12Month_10
Interaction_MYYear_12Month_11
Interaction_MYYear_12Month_12
Interaction_MYYear_13Month_2
Interaction_MYYear_13Month_4
Interaction_MYYear_13Month_6
Interaction_MYYear_13Month_11
Interaction_MYYear_13Month_12
Interaction_MYYear_14Month_1
Interaction_MYYear_14Month_3
Interaction_MYYear_14Month_5
Interaction_MYYear_14Month_6
Interaction_MYYear_14Month_7
Interaction_MYYear_14Month_9
Interaction_MYYear_14Month_10
Interaction_MYYear_14Month_11
Interaction_MYYear_15Month_1
Interaction_MYYear_15Month_3
Interaction_MYYear_15Month_6
Interaction_MYYear_15Month_7
Interaction_MYYear_15Month_8
Interaction_MYYear_15Month_9
Interaction_MYYear_15Month_10
Interaction_MYYear_15Month_11
Interaction_MYYear_16Month_1
Interaction_MYYear_16Month_2
Interaction_MYYear_16Month_3
Interaction_MYYear_16Month_4
Interaction_MYYear_16Month_5
Interaction_MYYear_16Month_6
Interaction_MYYear_16Month_7
Interaction_MYYear_16Month_8
Interaction_MYYear_16Month_9
Interaction_MYYear_16Month_10
Interaction_MYYear_16Month_11
Interaction_MYYear_16Month_12
Interaction_MYYear_17Month_1
Interaction_MYYear_17Month_2
Interaction_MYYear_17Month_3
Interaction_MYYear_17Month_4
Interaction_MYYear_17Month_5
Interaction_MYYear_17Month_6
Interaction_MYYear_17Month_7
Interaction_MYYear_17Month_8
Interaction_MYYear_17Month_9
Interaction_MYYear_17Month_10
Interaction_MYYear_17Month_11
Interaction_MYYear_17Month_12
Interaction_MYYear_18Month_1
Interaction_MYYear_18Month_2
Interaction_MYYear_18Month_3
Interaction_MYYear_18Month_4
Interaction_MYYear_18Month_5
Interaction_MYYear_18Month_6
Interaction_MYYear_18Month_7
Interaction_MYYear_18Month_8
Interaction_MYYear_18Month_9
Interaction_MYYear_18Month_12
Device_PossibleOwnership
AV_highrisk
AV_mediumrisk
AV_lowrisk
Wall time: 39.7 s
In [124]:
for col in different_columns:
    print("Number of Missing Values in Test set for "+str(col)+" : ",len(set(df_test[col].unique())-set(df[col].unique())))
    print("Number of Categories : ",df[col].nunique())
    print("Number of rows with missing categories in test data , ",df_test[df_test[col].isin(list(set(df_test[col].unique())-set(df[col].unique())))].shape[0])
    print("\n")
Number of Missing Values in Test set for EngineVersion :  1
Number of Categories :  70
Number of rows with missing categories in test data ,  3619770


Number of Missing Values in Test set for AppVersion :  10
Number of Categories :  107
Number of rows with missing categories in test data ,  2306148


Number of Missing Values in Test set for AvSigVersion :  1
Number of Categories :  8531
Number of rows with missing categories in test data ,  6598309


Number of Missing Values in Test set for RtpStateBitfield :  1
Number of Categories :  7
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for AVProductStatesIdentifier :  1
Number of Categories :  28969
Number of rows with missing categories in test data ,  16886


Number of Missing Values in Test set for CityIdentifier :  1
Number of Categories :  107366
Number of rows with missing categories in test data ,  52481


Number of Missing Values in Test set for OrganizationIdentifier :  1
Number of Categories :  49
Number of rows with missing categories in test data ,  7


Number of Missing Values in Test set for GeoNameIdentifier :  1
Number of Categories :  292
Number of rows with missing categories in test data ,  4


Number of Missing Values in Test set for OsVer :  1
Number of Categories :  58
Number of rows with missing categories in test data ,  13


Number of Missing Values in Test set for OsBuild :  20
Number of Categories :  61
Number of rows with missing categories in test data ,  7350


Number of Missing Values in Test set for OsBuildLab :  1
Number of Categories :  663
Number of rows with missing categories in test data ,  95381


Number of Missing Values in Test set for IeVerIdentifier :  1
Number of Categories :  303
Number of rows with missing categories in test data ,  27338


Number of Missing Values in Test set for SmartScreen :  1
Number of Categories :  7
Number of rows with missing categories in test data ,  163164


Number of Missing Values in Test set for UacLuaenable :  1
Number of Categories :  11
Number of rows with missing categories in test data ,  2


Number of Missing Values in Test set for Census_MDC2FormFactor :  1
Number of Categories :  13
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for Census_OEMNameIdentifier :  48
Number of Categories :  2137
Number of rows with missing categories in test data ,  147


Number of Missing Values in Test set for Census_OEMModelIdentifier :  1
Number of Categories :  175347
Number of rows with missing categories in test data ,  80321


Number of Missing Values in Test set for Census_ProcessorCoreCount :  4
Number of Categories :  45
Number of rows with missing categories in test data ,  4


Number of Missing Values in Test set for Census_ProcessorModelIdentifier :  1
Number of Categories :  2583
Number of rows with missing categories in test data ,  426


Number of Missing Values in Test set for Census_PrimaryDiskTotalCapacity :  3062
Number of Categories :  5734
Number of rows with missing categories in test data ,  3592


Number of Missing Values in Test set for Census_SystemVolumeTotalCapacity :  99275
Number of Categories :  536839
Number of rows with missing categories in test data ,  139515


Number of Missing Values in Test set for Census_TotalPhysicalRAM :  1830
Number of Categories :  3446
Number of rows with missing categories in test data ,  2363


Number of Missing Values in Test set for Census_ChassisTypeName :  1
Number of Categories :  52
Number of rows with missing categories in test data ,  6


Number of Missing Values in Test set for Census_InternalPrimaryDiagonalDisplaySizeInInches :  84
Number of Categories :  785
Number of rows with missing categories in test data ,  116


Number of Missing Values in Test set for Census_InternalPrimaryDisplayResolutionHorizontal :  386
Number of Categories :  2046
Number of rows with missing categories in test data ,  641


Number of Missing Values in Test set for Census_InternalPrimaryDisplayResolutionVertical :  233
Number of Categories :  1549
Number of rows with missing categories in test data ,  408


Number of Missing Values in Test set for Census_InternalBatteryNumberOfCharges :  11749
Number of Categories :  41087
Number of rows with missing categories in test data ,  17043


Number of Missing Values in Test set for Census_OSVersion :  1
Number of Categories :  469
Number of rows with missing categories in test data ,  2741713


Number of Missing Values in Test set for Census_OSBranch :  1
Number of Categories :  32
Number of rows with missing categories in test data ,  14


Number of Missing Values in Test set for Census_OSBuildNumber :  24
Number of Categories :  130
Number of rows with missing categories in test data ,  7783


Number of Missing Values in Test set for Census_OSBuildRevision :  1
Number of Categories :  285
Number of rows with missing categories in test data ,  2599915


Number of Missing Values in Test set for Census_OSEdition :  1
Number of Categories :  31
Number of rows with missing categories in test data ,  2


Number of Missing Values in Test set for Census_OSSkuName :  1
Number of Categories :  30
Number of rows with missing categories in test data ,  2


Number of Missing Values in Test set for Census_OSUILocaleIdentifier :  1
Number of Categories :  143
Number of rows with missing categories in test data ,  2


Number of Missing Values in Test set for Census_FlightRing :  1
Number of Categories :  10
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for Census_FirmwareManufacturerIdentifier :  1
Number of Categories :  712
Number of rows with missing categories in test data ,  309


Number of Missing Values in Test set for Census_FirmwareVersionIdentifier :  1
Number of Categories :  6569
Number of rows with missing categories in test data ,  1688


Number of Missing Values in Test set for Interaction_MYYear_9Month_8 :  1
Number of Categories :  1
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for Interaction_MYYear_12Month_2 :  1
Number of Categories :  1
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for Interaction_MYYear_14Month_4 :  1
Number of Categories :  1
Number of rows with missing categories in test data ,  1


Number of Missing Values in Test set for Interaction_MYYear_18Month_10 :  1
Number of Categories :  1
Number of rows with missing categories in test data ,  15364


Number of Missing Values in Test set for Interaction_MYYear_18Month_11 :  1
Number of Categories :  1
Number of rows with missing categories in test data ,  2025


Number of Missing Values in Test set for Interaction_01 :  2593
Number of Categories :  4465
Number of rows with missing categories in test data ,  3169


Number of Missing Values in Test set for Interaction_02 :  140338
Number of Categories :  565079
Number of rows with missing categories in test data ,  200917


Number of Missing Values in Test set for Interaction_03 :  7805
Number of Categories :  9615
Number of rows with missing categories in test data ,  8530


In [125]:
df.drop(columns=numerical_columns,inplace=True)
df_test.drop(columns=numerical_columns,inplace=True)
In [126]:
df.head()
Out[126]:
MachineIdentifier ProductName EngineVersion AppVersion AvSigVersion IsBeta RtpStateBitfield IsSxsPassiveMode AVProductStatesIdentifier AVProductsInstalled ... Interaction_MYYear_18Month_11 Interaction_MYYear_18Month_12 Device_PossibleOwnership AV_highrisk AV_mediumrisk AV_lowrisk Interaction_01 Interaction_02 Interaction_03 PPI
0 0000028988387b115f69f31a3bf04f09 0.0 0 0.0 0 0 0.0 0 0 1.0 ... 0 0 0 0 1 0 1024.0 177489.0 1296000.0 89.8125
1 000007535c3f730efa9ea0b7ef1bd645 0.0 1 5.0 1 0 0.0 0 0 1.0 ... 0 0 0 0 1 0 1024.0 374555.0 1049088.0 112.7500
2 000007905a28d863f6d0d597892cd692 0.0 0 0.0 2 0 0.0 0 0 1.0 ... 0 0 0 0 1 0 1024.0 566.0 2073600.0 102.4375
3 00000b11598a75ea8ba1beea8459149f 0.0 0 0.0 3 0 0.0 0 0 1.0 ... 0 0 1 0 1 0 1024.0 11359.0 1049088.0 84.6875
4 000014a5f00daa18e76b81417eeb99fc 0.0 0 0.0 4 0 0.0 0 0 1.0 ... 0 0 0 0 1 0 1536.0 375040.0 1049088.0 111.9375

5 rows × 186 columns

In [127]:
df_test.head()
Out[127]:
MachineIdentifier ProductName EngineVersion AppVersion AvSigVersion IsBeta RtpStateBitfield IsSxsPassiveMode AVProductStatesIdentifier AVProductsInstalled ... Interaction_MYYear_18Month_11 Interaction_MYYear_18Month_12 Device_PossibleOwnership AV_highrisk AV_mediumrisk AV_lowrisk Interaction_01 Interaction_02 Interaction_03 PPI
0 0000010489e3af074adeac69c53e555e 0.0 -1 2.0 -1 0 0.0 0 0 1.0 ... 0 0 0 0 1 0 2048.0 365207.0 2073600.0 142.1250
1 00000176ac758d54827acd545b6315a5 0.0 -1 1.0 -1 0 0.0 0 0 1.0 ... 0 0 0 0 1 0 2048.0 25377.0 1049088.0 101.1250
2 0000019dcefc128c2d4387c1273dae1d 0.0 3 1.0 -1 0 0.0 0 14 2.0 ... 0 0 0 0 0 0 2048.0 1142.0 921600.0 105.6875
3 0000055553dc51b1295785415f1a224d 0.0 -1 2.0 -1 0 0.0 0 79 2.0 ... 0 0 0 0 0 0 2048.0 37595.0 1049088.0 111.9375
4 00000574cefffeca83ec8adf9285b2bf 0.0 -1 1.0 -1 0 0.0 0 0 1.0 ... 0 0 0 0 1 0 512.0 15434.0 1049088.0 101.1250

5 rows × 185 columns

In [128]:
df.columns[df.isnull().any()]
Out[128]:
Index([], dtype='object')
In [130]:
df.drop(columns=['AVProductsEnabled','AVProductsInstalled'],inplace=True)
df_test.drop(columns=['AVProductsEnabled','AVProductsInstalled'],inplace=True)
In [131]:
#df,df_test=frequency_encode(df=df,df_test=df_test,col='Year')
#df,df_test=frequency_encode(df=df,df_test=df_test,col='Month')
In [132]:
df.shape
Out[132]:
(8920724, 184)
In [133]:
df_test.shape
Out[133]:
(7853253, 183)
In [134]:
for col in year_columns:
    if col in df.columns:
        df.drop(columns=[col],inplace=True)
        df_test.drop(columns=[col],inplace=True)
    else:
        continue
In [135]:
for col in month_columns:
    if col in df.columns:
        df.drop(columns=[col],inplace=True)
        df_test.drop(columns=[col],inplace=True)
    else:
        continue
In [136]:
set(df.columns)-set(df_test.columns)
Out[136]:
{'HasDetections'}
In [137]:
%%time
for col in df.columns[1:]:
    if col in ['MachineIdentifier','HasDetections']:
        continue
    elif df[col].nunique()==1:
        print(col)
        df.drop(columns=[col],inplace=True)
        df_test.drop(columns=[col],inplace=True)   
    else:
        continue
Interaction_MYYear_9Month_1
Interaction_MYYear_9Month_2
Interaction_MYYear_9Month_3
Interaction_MYYear_9Month_4
Interaction_MYYear_9Month_5
Interaction_MYYear_9Month_6
Interaction_MYYear_9Month_8
Interaction_MYYear_9Month_9
Interaction_MYYear_9Month_10
Interaction_MYYear_9Month_11
Interaction_MYYear_10Month_1
Interaction_MYYear_10Month_3
Interaction_MYYear_10Month_4
Interaction_MYYear_10Month_5
Interaction_MYYear_10Month_7
Interaction_MYYear_10Month_8
Interaction_MYYear_10Month_12
Interaction_MYYear_11Month_1
Interaction_MYYear_11Month_2
Interaction_MYYear_11Month_3
Interaction_MYYear_11Month_5
Interaction_MYYear_11Month_7
Interaction_MYYear_11Month_8
Interaction_MYYear_11Month_9
Interaction_MYYear_11Month_12
Interaction_MYYear_12Month_1
Interaction_MYYear_12Month_2
Interaction_MYYear_12Month_6
Interaction_MYYear_12Month_7
Interaction_MYYear_12Month_9
Interaction_MYYear_12Month_10
Interaction_MYYear_12Month_11
Interaction_MYYear_12Month_12
Interaction_MYYear_13Month_2
Interaction_MYYear_13Month_4
Interaction_MYYear_13Month_6
Interaction_MYYear_13Month_11
Interaction_MYYear_13Month_12
Interaction_MYYear_14Month_1
Interaction_MYYear_14Month_4
Interaction_MYYear_14Month_5
Interaction_MYYear_14Month_6
Interaction_MYYear_14Month_9
Interaction_MYYear_14Month_11
Interaction_MYYear_15Month_6
Interaction_MYYear_18Month_10
Interaction_MYYear_18Month_11
Interaction_MYYear_18Month_12
Wall time: 3min 2s
In [138]:
l=[]
for col in df.columns:
    if col in ['MachineIdentifier','HasDetections']:
        continue
    else:
        l.append(col)
l=['MachineIdentifier']+l+['HasDetections']
In [139]:
df=df[l]
In [148]:
#train=df.sample(frac=0.7,random_state=1)
In [186]:
#train.drop(columns=['AutoSampleOptIn','IsBeta','Census_IsPortableOperatingSystem','Census_IsFlightsDisabled'],inplace=True)
#df.drop(columns=['AutoSampleOptIn','IsBeta','Census_IsPortableOperatingSystem','Census_IsFlightsDisabled'],inplace=True)
In [ ]:
df_test.drop(columns=['AutoSampleOptIn','IsBeta','Census_IsPortableOperatingSystem','Census_IsFlightsDisabled'],inplace=True)
In [ ]:
df.shape
In [ ]:
#df.drop(columns=categorical_columns,inplace=True)
#df_test.drop(columns=categorical_columns,inplace=True)
In [ ]:
df.drop(columns=['AvSigVersion'],inplace=True)
#train.drop(columns=['AvSigVersion'],inplace=True)

df_test.drop(columns=['AvSigVersion'],inplace=True)
In [157]:
train=df.sample(frac=0.7,random_state=1)
In [158]:
X=train.iloc[:,1:-1]
y=train.iloc[:,-1]
In [187]:
from mlxtend.plotting import plot_learning_curves
from mlxtend.plotting import plot_decision_regions
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import BaggingClassifier
import matplotlib.gridspec as gridspec
from sklearn.model_selection import cross_val_score, train_test_split
In [160]:
from sklearn.preprocessing import StandardScaler
In [161]:
ss=StandardScaler()
In [162]:
ss.fit(X)
Out[162]:
StandardScaler(copy=True, with_mean=True, with_std=True)
In [163]:
X=pd.DataFrame(data=ss.transform(X),columns=X.columns.tolist())
In [164]:
set(X.columns)-set(df_test.columns)
Out[164]:
set()
In [165]:
df_test=pd.DataFrame(data=ss.transform(df_test[X.columns]),columns=X.columns.tolist())
In [166]:
#X.drop(columns=['Platform_w10', 'Platform_w2016', 'Platform_w8'],inplace=True)
In [167]:
X.shape
Out[167]:
(6244507, 112)
In [168]:
%%time
fig,ax=plt.subplots(figsize=(12,12))
corr=X.corr()

# plot the heatmap
sns.heatmap(corr,xticklabels=corr.columns,yticklabels=corr.columns,ax=ax)
Wall time: 2min 57s
In [169]:
cmap=sns.diverging_palette(5, 250, as_cmap=True)
def magnify():
    return [dict(selector="th",
                 props=[("font-size", "7pt")]),
            dict(selector="td",
                 props=[('padding', "0em 0em")]),
            dict(selector="th:hover",
                 props=[("font-size", "12pt")]),
            dict(selector="tr:hover td:hover",
                 props=[('max-width', '200px'),
                        ('font-size', '12pt')])
]

corr.style.background_gradient(cmap, axis=1)\
    .set_properties(**{'max-width': '80px', 'font-size': '10pt'})\
    .set_caption("Hover to magify")\
    .set_precision(2)\
    .set_table_styles(magnify())
C:\Users\gandh\Anaconda3\lib\site-packages\matplotlib\colors.py:504: RuntimeWarning: invalid value encountered in less
  xa[xa < 0] = -1
Out[169]:
Hover to magify
ProductName EngineVersion AppVersion RtpStateBitfield IsSxsPassiveMode AVProductStatesIdentifier HasTpm CountryIdentifier CityIdentifier OrganizationIdentifier GeoNameIdentifier LocaleEnglishNameIdentifier Platform Processor OsVer OsBuild OsSuite OsPlatformSubRelease OsBuildLab SkuEdition IsProtected SMode IeVerIdentifier SmartScreen Firewall UacLuaenable Census_MDC2FormFactor Census_DeviceFamily Census_OEMNameIdentifier Census_OEMModelIdentifier Census_ProcessorManufacturerIdentifier Census_ProcessorModelIdentifier Census_PrimaryDiskTypeName Census_HasOpticalDiskDrive Census_ChassisTypeName Census_PowerPlatformRoleName Census_OSVersion Census_OSArchitecture Census_OSBranch Census_OSBuildNumber Census_OSBuildRevision Census_OSEdition Census_OSSkuName Census_OSInstallTypeName Census_OSInstallLanguageIdentifier Census_OSUILocaleIdentifier Census_OSWUAutoUpdateOptionsName Census_GenuineStateName Census_ActivationChannel Census_FlightRing Census_ThresholdOptIn Census_FirmwareManufacturerIdentifier Census_FirmwareVersionIdentifier Census_IsSecureBootEnabled Census_IsWIMBootEnabled Census_IsVirtualDevice Census_IsTouchEnabled Census_IsPenCapable Census_IsAlwaysOnAlwaysConnectedCapable Wdft_IsGamer Wdft_RegionIdentifier Interaction_MYYear_14Month_3 Interaction_MYYear_14Month_7 Interaction_MYYear_14Month_10 Interaction_MYYear_15Month_1 Interaction_MYYear_15Month_3 Interaction_MYYear_15Month_7 Interaction_MYYear_15Month_8 Interaction_MYYear_15Month_9 Interaction_MYYear_15Month_10 Interaction_MYYear_15Month_11 Interaction_MYYear_16Month_1 Interaction_MYYear_16Month_2 Interaction_MYYear_16Month_3 Interaction_MYYear_16Month_4 Interaction_MYYear_16Month_5 Interaction_MYYear_16Month_6 Interaction_MYYear_16Month_7 Interaction_MYYear_16Month_8 Interaction_MYYear_16Month_9 Interaction_MYYear_16Month_10 Interaction_MYYear_16Month_11 Interaction_MYYear_16Month_12 Interaction_MYYear_17Month_1 Interaction_MYYear_17Month_2 Interaction_MYYear_17Month_3 Interaction_MYYear_17Month_4 Interaction_MYYear_17Month_5 Interaction_MYYear_17Month_6 Interaction_MYYear_17Month_7 Interaction_MYYear_17Month_8 Interaction_MYYear_17Month_9 Interaction_MYYear_17Month_10 Interaction_MYYear_17Month_11 Interaction_MYYear_17Month_12 Interaction_MYYear_18Month_1 Interaction_MYYear_18Month_2 Interaction_MYYear_18Month_3 Interaction_MYYear_18Month_4 Interaction_MYYear_18Month_5 Interaction_MYYear_18Month_6 Interaction_MYYear_18Month_7 Interaction_MYYear_18Month_8 Interaction_MYYear_18Month_9 Device_PossibleOwnership AV_highrisk AV_mediumrisk AV_lowrisk Interaction_01 Interaction_02 Interaction_03 PPI
ProductName 1 -0.0078 0.086 -0.0079 -0.014 0.046 -0.94 -0.028 0.023 -0.00019 -0.022 -0.0054 0.29 0.073 0.3 0.37 -0.028 0.4 0.28 0.2 0.0095 -0.0021 0.38 -0.028 0.0096 0.17 -0.021 -0.0038 0.0063 0.037 0.0082 0.046 0.03 -0.0049 -0.006 0.014 0.092 0.072 0.037 0.15 0.094 -0.042 -0.037 0.011 0.0048 0.014 0.008 -0.012 -0.093 -0.019 0.0006 0.012 0.024 -0.093 nan -0.0044 -0.03 -0.017 -0.025 -0.062 0.047 0.017 0.0016 -0.00083 0.0098 0.046 -0.0041 -0.0027 0.002 -0.0019 -0.0041 0.0045 0.0064 0.016 0.11 -0.0012 -0.0038 -0.0086 -0.004 0.014 0.0069 -0.0052 -0.0073 -0.00093 0.037 -0.031 0.0036 0.059 -0.016 -0.0017 0.039 -0.033 -0.0044 -0.0055 0.01 0.084 -0.005 0.0031 -0.084 -0.022 0.25 -0.0086 0.32 -0.0024 -0.031 -4.1e-05 0.022 -0.01 -0.014 -5.9e-05 -0.027 -0.04
EngineVersion -0.0078 1 0.31 0.017 -0.0036 0.04 0.0042 0.00037 -0.0014 -0.005 0.0013 -0.00021 0.00091 0.034 0.0046 0.1 0.032 0.11 0.13 -0.032 -0.23 -0.00068 0.11 -0.021 0.0044 -0.0049 -0.013 -0.011 -0.0016 -0.00088 0.011 0.0063 -0.018 0.0026 0.0069 -0.022 0.14 0.034 0.11 0.11 0.14 -0.0052 0.014 0.037 -0.0041 -0.013 -0.0029 0.0075 -0.0094 0.019 0.0011 0.011 -0.007 0.02 nan -0.0025 0.0015 -0.012 0.0016 -0.084 0.001 0.0043 0.0043 0.0043 0.0031 0.0022 0.015 0.0076 0.0047 0.0076 0.017 0.011 0.02 0.027 0.022 0.028 0.022 0.013 0.036 0.037 0.052 0.032 0.048 0.0011 0.013 0.059 0.041 0.0092 0.028 0.055 0.011 0.069 0.02 0.028 0.021 0.015 0.015 -0.0019 -0.12 -0.041 -0.042 -0.011 0.011 0.012 -0.021 -2.9e-05 -0.09 0.03 -0.036 -4.1e-05 -0.045 -0.0067
AppVersion 0.086 0.31 1 -0.0011 -0.021 0.041 -0.081 -0.026 0.0037 -0.0082 -0.026 -0.015 0.084 0.071 0.09 0.39 0.021 0.41 0.43 0.05 -0.2 -0.0093 0.47 -0.016 -0.0013 0.015 -0.0026 -0.013 0.0021 -0.0011 0.011 0.013 -0.0018 0.0038 0.0083 -0.022 0.37 0.071 0.32 0.39 0.36 0.05 0.039 0.067 -0.023 -0.034 -0.037 0.054 0.02 0.075 0.0024 0.015 -0.0075 0.0085 nan -0.0026 0.0054 -0.022 0.015 -0.12 0.0067 0.048 0.036 0.051 0.034 0.033 0.13 0.057 0.03 0.069 0.048 0.053 0.065 0.056 0.085 0.044 0.11 0.056 0.12 0.11 0.16 0.063 0.06 0.029 0.03 0.15 0.13 0.0066 0.095 0.18 0.0082 0.033 0.04 0.05 0.031 0.015 0.028 -0.002 -0.29 -0.11 0.043 0.028 0.0096 0.026 -0.0028 -0.00023 -0.099 0.026 -0.064 -8.3e-05 -0.083 -0.011
RtpStateBitfield -0.0079 0.017 -0.0011 1 0.53 0.11 0.0081 -0.00042 -0.0021 4.3e-05 0.004 0.00062 -0.00091 -0.012 -0.00052 -0.019 0.012 -0.021 -0.015 -0.015 -0.14 -0.0026 -0.022 0.02 -0.0091 0.0044 -0.015 -0.00012 -0.0074 -0.0028 0.00034 -0.0062 -0.012 0.0029 -0.0059 -0.013 -0.0098 -0.012 -0.02 -0.02 -0.0094 0.0034 0.01 0.0006 0.0045 0.00037 0.0088 -0.0086 0.0077 0.00013 -0.00053 -0.0056 -0.0049 0.017 nan -0.0079 -0.0087 -0.0054 -0.018 0.01 -0.007 -0.00092 -0.00091 -0.001 -0.00046 -0.00039 -0.0074 -0.0033 -0.0018 -0.0064 -0.0048 -0.004 -0.0052 -0.0032 -0.0039 -0.0035 -0.0039 0.0014 -0.0033 -0.0012 -0.0028 2.7e-05 0.0024 -0.00022 0.00032 -0.0062 -0.0017 -0.00023 -0.015 -0.0033 0.00023 -0.0047 -0.00098 0.0012 -0.0024 -0.00054 -0.0013 0.00024 0.011 0.01 -0.0025 0.0015 -0.0018 -0.00016 0.016 0.0048 -0.14 0.084 0.011 -8.1e-05 0.005 -0.0045
IsSxsPassiveMode -0.014 -0.0036 -0.021 0.53 1 0.12 0.014 0.0082 -0.0015 0.00066 0.0077 0.0038 -0.024 -0.013 -0.022 -0.038 0.015 -0.042 -0.024 -0.02 -0.035 -0.0028 -0.034 0.028 -0.0019 6.6e-05 -0.015 -0.0053 -0.0074 -0.002 -0.0018 -0.0095 -0.0057 0.0086 -0.0081 -0.011 -0.021 -0.013 -0.039 -0.035 -0.02 -7.7e-05 0.0097 -0.0025 0.0068 0.0029 0.019 -0.024 0.013 -0.0036 -0.00054 -0.0073 -0.0037 0.02 nan -0.01 -0.0061 -0.0013 -0.016 0.031 0.0014 -0.0011 -0.00084 -0.0011 -0.00066 -0.001 -0.0083 -0.0036 -0.0018 -0.0079 -0.0054 -0.0054 -0.0071 -0.0041 -0.0053 -0.005 -0.0055 0.00028 -0.0039 -0.0017 -0.0037 0.00095 0.0043 2.1e-05 -0.0017 -0.012 -0.0013 -0.0016 -0.02 -0.003 -0.0016 -0.016 -0.0011 0.0033 -0.002 -0.0026 -0.002 0.003 0.028 0.012 -0.013 -0.011 -0.0052 0.00029 0.022 -5.3e-05 -0.2 0.044 0.017 -7.5e-05 0.018 0.0033
AVProductStatesIdentifier 0.046 0.04 0.041 0.11 0.12 1 -0.041 0.00087 0.006 0.00056 0.0078 0.0082 0.026 -0.0043 0.029 0.029 0.021 0.033 0.024 -0.012 -0.068 -0.0024 0.026 -0.0034 -0.021 0.009 -0.017 -0.0046 -0.0071 0.0095 0.0038 -0.002 -0.015 0.0076 -0.008 -0.013 0.016 -0.0043 0.0082 0.018 0.018 -0.0057 0.012 -0.0074 0.023 0.017 0.033 -0.021 -0.0043 -0.002 9.8e-05 -0.00056 0.00015 0.023 nan -0.0082 -0.0086 -0.0079 -0.018 -0.0057 0.015 0.0073 0.0077 0.0058 0.0043 0.0042 -0.0022 -0.00086 0.00048 -0.0039 -0.0013 -0.00058 -0.00028 0.0031 0.0061 0.00069 -0.00012 -0.0032 0.0042 0.004 0.0052 0.0029 0.0072 -0.00088 0.0024 -0.00068 0.0037 0.0055 0.00089 0.0043 0.0043 -0.0029 0.0013 0.0056 0.0035 0.0069 9.4e-05 0.0037 -0.02 0.013 0.014 0.0054 0.012 -0.00041 -0.0073 0.0067 -0.17 0.25 0.0027 -6.5e-05 -0.0043 -0.011
HasTpm -0.94 0.0042 -0.081 0.0081 0.014 -0.041 1 0.029 -0.022 -0.00041 0.023 0.0071 -0.29 -0.067 -0.29 -0.35 0.024 -0.38 -0.27 -0.19 -0.0091 0.0023 -0.36 0.026 -0.0092 -0.16 0.019 -0.015 -0.0056 -0.038 -0.0059 -0.045 -0.029 0.0051 0.00077 -0.013 -0.093 -0.066 -0.037 -0.14 -0.094 0.033 0.028 -0.014 -0.0046 -0.013 -0.0092 0.0075 0.091 0.017 -0.00044 -0.01 -0.023 0.086 nan 0.0021 0.029 0.017 0.026 0.062 -0.043 -0.018 -0.0042 -0.0035 -0.01 -0.044 0.0044 0.0028 -0.0019 0.002 0.0042 -0.0041 -0.0059 -0.015 -0.1 0.001 0.0032 0.0088 0.0035 -0.013 -0.0063 0.0052 0.0068 0.00099 -0.036 0.03 -0.0034 -0.056 0.017 0.0021 -0.037 0.026 0.0039 0.0052 -0.0096 -0.08 0.0043 -0.0029 0.085 0.021 -0.24 0.003 -0.3 0.0026 0.03 4.4e-05 -0.026 0.011 0.012 6.3e-05 0.025 0.039
CountryIdentifier -0.028 0.00037 -0.026 -0.00042 0.0082 0.00087 0.029 1 -0.1 -0.036 0.36 0.25 -0.048 -0.0078 -0.046 -0.077 -0.0073 -0.08 -0.041 -0.0056 -0.034 0.0018 -0.054 0.0062 0.013 -0.013 -0.028 -0.008 -0.028 -0.013 -0.021 -0.032 -0.028 0.025 -0.0053 -0.034 -0.071 -0.0078 -0.076 -0.064 -0.07 -0.12 -0.084 -0.15 -0.061 0.0068 -0.0066 -0.0053 0.047 -0.022 -0.002 0.0075 -0.035 0.019 nan -0.0096 0.031 0.0011 -0.014 0.021 -0.005 -0.0029 -0.0023 -0.0032 -0.0023 -0.0028 0.03 0.014 0.0034 -0.0031 0.0095 -0.0044 0.0021 0.0027 -0.0032 0.0031 0.0041 -0.0094 -0.00078 0.0037 -0.005 -0.0088 -0.0099 0.0017 -0.0039 -0.042 -0.0078 -0.0035 0.014 -0.0075 -0.0036 -0.034 -0.0059 -0.0068 -0.006 -0.0044 -0.006 -0.023 0.12 -0.062 -0.03 -0.023 -0.028 -0.0094 0.021 -0.00024 -0.048 0.011 0.019 5.4e-05 -0.015 -0.013
CityIdentifier 0.023 -0.0014 0.0037 -0.0021 -0.0015 0.006 -0.022 -0.1 1 -0.018 -0.066 -0.021 0.015 -0.0021 0.012 0.019 0.01 0.018 0.014 -0.0027 0.022 0.0036 0.014 -0.0098 0.0088 0.0013 0.009 0.0062 0.012 0.022 0.031 0.034 0.033 -0.0078 -0.011 0.027 0.015 -0.0022 0.0074 0.01 0.015 -0.069 -0.026 0.031 0.044 0.08 0.027 -0.028 -0.028 0.006 0.0028 0.0037 0.017 -0.0076 nan 0.0014 -0.0084 2.9e-05 0.0074 -0.0086 0.2 -2.4e-05 0.00048 -8.3e-05 -0.00034 0.00082 -0.01 -0.0038 -0.00095 -0.0032 -0.0047 -4.4e-05 0.00041 4.5e-05 0.0046 0.00028 0.00019 -0.0028 0.0023 -0.00016 0.0042 0.0024 0.006 -0.0004 0.0001 0.003 0.002 0.00083 -0.0091 0.0021 0.0021 0.0015 0.0028 0.0018 0.0015 0.0014 0.0015 0.00097 -0.018 0.015 0.012 0.0038 0.013 0.0044 -0.026 0.00046 0.0039 0.0023 0.026 0.00033 0.015 -0.02
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Platform 0.29 0.00091 0.084 -0.00091 -0.024 0.026 -0.29 -0.048 0.015 0.0021 -0.028 -0.015 1 0.0022 0.87 0.46 0.08 0.57 0.3 0.089 -0.035 -0.0037 0.17 0.033 -0.033 0.054 0.052 0.36 0.003 0.02 0.011 0.083 -0.0035 -0.0055 0.036 0.023 0.19 0.0035 0.051 0.23 0.19 0.1 0.14 0.011 0.0025 -0.001 0.072 -0.0043 -0.14 -0.028 0.0013 0.013 0.019 0.024 nan 0.061 -0.0012 -0.013 -0.024 -0.1 0.033 0.044 0.036 0.047 0.026 0.032 -0.0096 -0.0044 -0.00036 -0.009 -0.0061 0.00063 -0.0058 0.022 0.029 -0.0058 0.00016 -0.014 0.01 0.014 -0.0091 -0.0084 -0.012 -0.0016 0.062 -0.056 0.0025 0.052 -0.024 -0.0006 0.062 -0.056 0.014 -0.0059 -0.0016 0.087 0.032 0.013 -0.14 -0.038 0.48 0.49 0.21 -0.0042 0.023 -7.2e-05 0.013 -0.02 0.0079 0.0016 -0.036 -0.033
Processor 0.073 0.034 0.071 -0.012 -0.013 -0.0043 -0.067 -0.0078 -0.0021 -0.0081 -0.028 -0.00075 0.0022 1 0.008 0.09 -0.091 0.095 0.21 0.076 -0.0098 -0.0042 0.11 -0.031 0.013 0.01 0.23 -0.013 0.13 0.063 -0.011 0.13 0.18 0.016 0.027 0.22 0.053 0.99 0.11 0.085 0.056 -0.065 -0.093 0.028 0.015 -0.0099 0.043 0.015 -0.12 0.004 0.0013 0.078 0.093 -0.23 nan -0.006 0.076 -0.023 0.3 -0.043 -0.0089 0.0009 -0.0012 -0.00083 -0.00035 0.0057 0.0023 0.005 0.0036 0.0095 0.011 0.01 0.0052 0.0068 0.016 0.0075 0.0064 -0.0039 0.011 0.0054 0.013 0.0042 0.0049 0.0048 0.0026 -0.01 0.0071 0.0037 0.038 0.013 0.0019 0.15 0.004 0.017 0.012 0.015 0.011 0.046 -0.074 -0.13 0.013 -0.012 0.023 -0.0023 -0.053 -0.00013 0.05 -0.021 -0.19 -0.00018 -0.11 0.0088
OsVer 0.3 0.0046 0.09 -0.00052 -0.022 0.029 -0.29 -0.046 0.012 -0.0016 -0.024 -0.012 0.87 0.008 1 0.45 0.027 0.56 0.26 0.027 -0.039 -0.0034 0.17 -0.013 -0.0065 0.052 -0.017 -0.0062 -0.0027 0.017 0.015 0.05 -0.025 -0.0059 -0.0059 -0.019 0.14 0.0092 0.057 0.21 0.14 -0.0095 0.026 0.019 0.0071 0.0043 0.004 -0.0069 -0.15 -0.025 0.0014 0.0012 0.0066 0.034 nan -0.011 0.0015 -0.014 -0.02 -0.093 0.028 0.044 0.036 0.047 0.026 0.068 -0.0087 -0.0039 -0.0002 -0.0082 -0.0055 0.0012 -0.005 0.023 0.029 -0.0054 0.00073 -0.013 0.011 0.015 -0.0081 -0.0087 -0.013 -0.0015 0.062 -0.052 0.0022 0.052 -0.023 -0.0021 0.062 -0.051 -0.0071 -0.0091 -0.004 0.082 0.027 0.011 -0.13 -0.032 0.46 0.46 0.18 -0.0039 -0.00044 -6.7e-05 0.0031 -0.018 -0.015 0.0016 -0.037 -0.027
OsBuild 0.37 0.1 0.39 -0.019 -0.038 0.029 -0.35 -0.077 0.019 -0.01 -0.063 -0.039 0.46 0.09 0.45 1 0.041 0.93 0.65 0.096 -0.11 -0.012 0.74 -0.017 0.0011 0.058 0.032 0.036 0.023 0.004 0.017 0.035 0.016 -0.0012 0.019 -0.012 0.55 0.09 0.56 0.86 0.5 0.048 0.072 0.12 -0.037 -0.048 -0.033 0.058 -0.008 0.25 0.037 0.03 0.0035 0.0057 nan 0.01 0.026 -0.024 0.057 -0.13 0.054 0.023 0.016 0.02 0.014 0.044 0.12 0.053 0.027 0.087 0.06 0.061 0.08 0.053 0.084 0.055 0.062 0.077 0.064 0.066 0.097 0.053 0.069 0.0082 0.036 0.13 0.066 0.037 0.3 0.09 0.036 0.055 0.039 0.052 0.054 0.063 0.055 0.17 -0.59 -0.037 0.29 0.22 0.25 0.15 -0.0063 -0.00027 -0.0042 -0.013 -0.091 0.00051 -0.11 -0.0029
OsSuite -0.028 0.032 0.021 0.012 0.015 0.021 0.024 -0.0073 0.01 -0.02 0.084 0.057 0.08 -0.091 0.027 0.041 1 0.056 0.034 -0.77 -0.028 -0.0015 0.017 -0.017 0.049 -0.036 0.018 0.14 -0.012 -0.091 0.033 -0.13 -0.16 -0.041 0.0097 -0.16 0.075 -0.09 0.063 0.041 0.085 -0.14 0.5 0.17 0.055 0.033 0.13 -0.21 0.16 -0.057 -0.0075 0.02 -0.082 0.45 nan -0.038 0.091 -0.053 0.056 -0.017 -0.028 0.0038 0.0045 0.0044 0.0021 0.00042 -0.055 -0.013 0.0034 -0.026 -0.0047 -0.01 0.0042 0.0092 0.0055 0.0004 -0.017 -0.0081 0.016 0.014 0.026 0.0032 0.018 0.00067 0.00028 0.068 0.015 -0.00093 -0.059 0.029 0.00028 0.01 0.022 0.018 0.019 0.0013 0.018 0.052 -0.12 0.088 0.011 0.027 -0.0077 -0.017 -0.026 0.0003 -0.17 0.064 -0.053 0.00042 -0.099 0.074
OsPlatformSubRelease 0.4 0.11 0.41 -0.021 -0.042 0.033 -0.38 -0.08 0.018 -0.013 -0.064 -0.036 0.57 0.095 0.56 0.93 0.056 1 0.61 0.094 -0.12 -0.015 0.68 -0.02 0.0007 0.062 0.031 0.039 0.023 0.0021 0.018 0.035 0.0076 -0.0028 0.019 -0.02 0.53 0.096 0.56 0.78 0.52 0.053 0.086 0.15 -0.039 -0.05 -0.035 0.063 -0.011 0.13 0.013 0.033 -0.0011 0.02 nan 0.0035 0.028 -0.029 0.06 -0.15 0.051 0.029 0.021 0.026 0.017 0.029 0.13 0.057 0.03 0.094 0.065 0.067 0.087 0.059 0.091 0.059 0.068 0.083 0.071 0.074 0.1 0.057 0.074 0.0089 0.043 0.14 0.072 0.043 0.33 0.099 0.044 0.062 0.042 0.057 0.059 0.074 0.062 0.19 -0.63 -0.041 0.34 0.27 0.22 0.061 -0.0055 -0.0003 -0.0096 -0.014 -0.1 0.00072 -0.13 -0.0054
OsBuildLab 0.28 0.13 0.43 -0.015 -0.024 0.024 -0.27 -0.041 0.014 -0.007 -0.04 -0.019 0.3 0.21 0.26 0.65 0.034 0.61 1 0.12 -0.11 -0.0069 0.66 -0.0011 -0.0066 0.054 0.099 0.13 0.044 0.015 0.0043 0.054 0.058 -0.0013 0.032 0.053 0.59 0.21 0.28 0.56 0.56 0.073 0.07 0.089 -0.025 -0.031 -0.0056 0.022 -0.018 0.13 0.023 0.037 0.027 -0.03 nan 0.026 0.043 -0.012 0.1 -0.091 0.041 0.068 0.044 0.065 0.043 0.067 0.04 0.15 0.1 0.067 0.12 0.078 0.076 0.1 0.04 0.031 0.066 -0.0069 0.13 0.16 0.19 0.087 0.14 0.076 0.037 0.021 0.14 0.074 0.11 0.17 0.089 -0.018 0.08 0.13 0.1 0.099 0.1 0.07 -0.33 -0.12 0.085 0.1 0.21 0.14 -0.0096 -0.00016 0.0069 -0.014 -0.085 -8.8e-05 -0.086 0.0056
SkuEdition 0.2 -0.032 0.05 -0.015 -0.02 -0.012 -0.19 -0.0056 -0.0027 0.018 -0.088 -0.059 0.089 0.076 0.027 0.096 -0.77 0.094 0.12 1 0.024 -0.00048 0.11 0.061 -0.063 0.066 0.028 0.17 0.028 0.079 -0.025 0.14 0.16 0.034 0.022 0.15 0.04 0.076 -0.022 0.063 0.034 0.44 -0.09 -0.15 -0.057 -0.039 -0.083 0.19 -0.081 0.08 0.0056 -0.0048 0.08 -0.4 nan 0.1 -0.083 0.037 -0.057 -0.00042 0.044 -6e-05 -0.0035 -0.004 -0.00025 0.01 0.095 0.012 0.00026 0.016 0.0011 0.0076 -0.0041 -0.006 0.018 -0.003 0.01 0.0043 -0.015 -0.01 -0.023 -0.004 -0.018 -0.0013 0.0058 -0.077 -0.013 0.01 0.12 -0.026 0.0072 -0.034 -0.0074 -0.013 -0.013 0.022 -0.0064 -0.044 0.058 -0.087 0.074 0.0068 0.13 0.042 0.05 -0.00026 0.16 -0.057 0.056 -0.00037 0.071 -0.076
IsProtected 0.0095 -0.23 -0.2 -0.14 -0.035 -0.068 -0.0091 -0.034 0.022 0.011 -0.0038 0.00034 -0.035 -0.0098 -0.039 -0.11 -0.028 -0.12 -0.11 0.024 1 0.0039 -0.11 -0.00041 0.0055 0.0022 0.025 0.0095 0.0085 0.017 -0.0019 0.018 0.048 -0.0086 0.0027 0.042 -0.11 -0.0099 -0.12 -0.11 -0.11 -0.025 -0.027 -0.033 0.046 0.046 0.013 -0.0063 -0.029 -0.021 0.00058 -0.01 0.021 -0.038 nan 0.012 -0.0018 0.012 0.0048 0.034 0.0076 -0.03 -0.025 -0.032 -0.011 -0.004 -0.036 -0.015 -0.0083 -0.013 -0.023 -0.011 -0.023 -0.026 -0.014 -0.019 -0.014 -0.018 -0.029 -0.03 -0.036 -0.022 -0.033 -0.00082 -0.015 -0.051 -0.028 -0.0077 -0.046 -0.039 -0.013 -0.058 -0.013 -0.019 -0.014 -0.011 -0.015 -0.0021 0.12 0.023 0.0018 0.0005 0.02 0.0052 -0.01 9.6e-05 0.047 -0.038 0.037 0.00013 0.052 0.0043
SMode -0.0021 -0.00068 -0.0093 -0.0026 -0.0028 -0.0024 0.0023 0.0018 0.0036 0.00088 0.00094 -0.0019 -0.0037 -0.0042 -0.0034 -0.012 -0.0015 -0.015 -0.0069 -0.00048 0.0039 1 -0.0099 -0.0016 0.0026 -0.0014 0.0035 -0.00083 1.5e-05 -0.0009 -0.0043 0.0051 0.015 -0.0055 0.015 -0.0038 -0.0077 -0.0041 -0.016 -0.013 -0.008 -0.0084 -0.0061 0.012 -0.0023 0.0005 -0.008 0.0016 0.0063 0.0084 -0.0002 0.007 0.00037 0.017 nan -0.0013 0.016 0.024 0.015 -0.0019 0.01 -0.00018 -0.00013 -0.00017 -0.0001 -0.00016 -0.0013 -0.00056 -0.00028 -0.0012 -0.00085 -0.00084 -0.0011 -0.00064 -0.00083 -0.00078 -0.00086 -0.0017 -0.00095 -0.001 -0.0016 -0.0011 -0.0016 -0.00019 -0.00026 -0.0066 -0.0012 -0.00025 -0.0034 -0.0017 -0.00027 -0.0091 -0.00089 -0.0012 -0.0011 -0.00052 -0.001 -0.0036 0.023 -0.0084 -0.0019 -0.0017 -0.0016 0.00051 -0.0049 -8.3e-06 0.013 -0.0026 0.0015 -1.2e-05 0.0028 0.019
IeVerIdentifier 0.38 0.11 0.47 -0.022 -0.034 0.026 -0.36 -0.054 0.014 -0.008 -0.048 -0.029 0.17 0.11 0.17 0.74 0.017 0.68 0.66 0.11 -0.11 -0.0099 1 -0.022 0.0051 0.068 0.035 0.023 0.024 0.0062 0.0076 0.024 0.035 0.0011 0.016 0.0026 0.52 0.11 0.57 0.66 0.47 0.039 0.04 0.11 -0.038 -0.041 -0.021 0.05 0.016 0.2 0.029 0.029 0.0064 -0.023 nan 0.011 0.022 -0.018 0.061 -0.099 0.043 0.099 0.064 0.061 0.044 0.075 0.072 0.064 0.037 0.13 0.077 0.057 0.071 0.056 0.095 0.039 0.095 -0.037 0.039 0.043 0.16 0.0021 -0.034 0.077 0.054 0.17 0.096 0.077 0.2 0.12 0.11 0.14 0.072 0.097 0.034 0.054 0.033 0.095 -0.47 -0.093 0.1 0.018 0.16 0.16 -0.015 -0.00022 0.0015 -0.011 -0.085 4.4e-06 -0.094 0.0034
SmartScreen -0.028 -0.021 -0.016 0.02 0.028 -0.0034 0.026 0.0062 -0.0098 0.0016 0.004 0.011 0.033 -0.031 -0.013 -0.017 -0.017 -0.02 -0.0011 0.061 -0.00041 -0.0016 -0.022 1 -0.041 0.0046 0.0071 0.12 -0.00057 -0.0056 -0.0042 0.0077 -2.2e-06 0.032 0.0037 0.0068 -0.001 -0.031 -0.027 -0.01 -0.00086 0.073 0.039 -0.061 0.0043 -0.0049 0.039 -0.0098 0.019 0.013 0.0029 -0.0057 0.0017 -0.026 nan 0.022 -0.014 -0.0078 -0.026 0.016 -0.029 -0.0013 -0.00069 -0.00091 -0.00042 -0.0012 0.0028 0.0022 -0.00023 0.00043 0.00016 -0.0015 -0.0016 -0.003 -0.0061 -0.0022 -0.00063 0.0014 -0.0049 -0.0053 -0.0088 -0.0019 -0.0056 0.00077 -0.00075 -0.025 -0.0043 -0.0021 0.0077 -0.0069 -0.00039 -0.014 -0.0023 -0.0021 -0.0021 0.0015 0.0014 -0.0067 0.018 0.013 0.0032 0.012 0.0028 0.0012 0.8 -0.00014 0.027 -0.017 0.033 0.0002 0.0052 -0.017
Firewall 0.0096 0.0044 -0.0013 -0.0091 -0.0019 -0.021 -0.0092 0.013 0.0088 -0.0092 0.017 0.018 -0.033 0.013 -0.0065 0.0011 0.049 0.0007 -0.0066 -0.063 0.0055 0.0026 0.0051 -0.041 1 -0.037 -0.026 -0.075 -0.0067 -0.011 0.016 -0.015 -0.0086 0.00098 -0.0094 -0.046 -0.0041 0.013 0.0097 0.00042 -0.0023 -0.048 -0.0043 0.024 0.03 0.028 -0.00047 -0.021 -0.015 -0.0023 -5.2e-05 0.0061 -0.015 0.031 nan -0.043 0.016 0.0098 0.019 0.0057 0.0088 -0.00084 -0.00048 -0.00061 -0.0006 0.00017 -0.0034 -0.00043 0.00034 -0.0033 0.00049 -0.0015 -0.00046 -0.0011 0.0017 0.00018 -0.00045 -0.0032 0.0019 0.00081 0.0025 0.00022 0.0031 -0.00027 -0.0025 0.0046 0.0014 -0.00025 -0.0047 0.0037 -0.0021 0.01 -0.0023 0.0021 0.0026 -0.0012 0.00053 0.0055 -0.014 0.011 -0.0077 -0.0098 -0.008 -0.00058 -0.03 5.9e-05 0.0031 0.0064 -0.014 8.1e-05 -0.007 0.03
UacLuaenable 0.17 -0.0049 0.015 0.0044 6.6e-05 0.009 -0.16 -0.013 0.0013 0.005 -0.016 -0.013 0.054 0.01 0.052 0.058 -0.036 0.062 0.054 0.066 0.0022 -0.0014 0.068 0.0046 -0.037 1 0.0041 0.0085 0.01 0.014 -0.0048 0.015 0.015 -0.0016 -0.0042 0.021 0.015 0.011 -0.0024 0.02 0.015 0.013 -0.013 -0.0088 -0.00065 0.0017 -0.0018 0.0067 -0.015 -0.0011 0.0005 -0.001 0.019 -0.035 nan 0.0049 -0.011 -0.0057 -0.0097 -0.0077 0.0089 0.0046 0.00079 -0.00012 0.0041 0.0086 -0.0021 -0.0013 0.00016 -0.0011 -0.00044 0.0023 -5.3e-05 0.0017 0.022 -0.00093 -0.0021 -0.0031 -0.0025 0.0021 -0.00013 -0.0018 -0.0033 0.0033 0.011 -0.0085 -0.00015 0.015 -0.008 -0.0028 0.0094 -0.011 -0.0017 -0.0022 0.00076 0.016 -0.0019 -0.0045 -0.0045 -0.0072 0.043 0.0017 0.066 0.0028 -0.004 -3.1e-05 0.0082 -0.0033 0.0034 -4.3e-05 0.0054 -0.017
Census_MDC2FormFactor -0.021 -0.013 -0.0026 -0.015 -0.015 -0.017 0.019 -0.028 0.009 0.0096 -0.0043 -0.011 0.052 0.23 -0.017 0.032 0.018 0.031 0.099 0.028 0.025 0.0035 0.035 0.0071 -0.026 0.0041 1 0.19 0.17 0.056 -0.053 0.081 0.24 -0.057 0.27 0.65 0.059 0.23 0.032 0.043 0.058 0.011 0.022 0.055 -0.0012 -0.0076 0.058 -0.02 0.082 0.0051 0.0012 0.037 0.099 -0.019 nan 0.049 0.51 0.33 0.66 -0.018 0.034 -0.0012 -0.0016 -0.00093 -0.00014 -0.0018 -0.014 0.00016 0.0041 0.0024 0.0052 0.0042 0.0084 0.0068 0.0031 -0.00043 0.0032 -0.00081 0.0057 -0.0026 0.011 0.0048 -0.0024 0.013 -0.0015 0.0059 0.0015 -0.002 0.00011 0.0098 -0.002 0.034 0.014 0.008 0.0096 0.002 0.014 0.041 -0.012 -0.065 0.0048 0.0083 0.015 0.0014 -0.022 -0.00022 0.087 -0.03 -0.11 -0.00031 0.2 0.28
Census_DeviceFamily -0.0038 -0.011 -0.013 -0.00012 -0.0053 -0.0046 -0.015 -0.008 0.0062 0.009 -0.012 -0.0079 0.36 -0.013 -0.0062 0.036 0.14 0.039 0.13 0.17 0.0095 -0.00083 0.023 0.12 -0.075 0.0085 0.19 1 0.019 0.01 -0.0099 0.09 0.059 0.0029 0.11 0.12 0.13 -0.013 -0.016 0.047 0.13 0.31 0.32 -0.021 -0.011 -0.013 0.18 0.0079 0.023 -0.0087 -0.00039 0.036 0.034 -0.032 nan 0.2 -0.0099 0.0015 -0.0098 -0.024 0.014 -0.00034 -0.00025 -0.00032 -0.0002 -0.00032 -0.0025 -0.0011 -0.00054 -0.0024 -0.0016 -0.0016 -0.0021 -0.00099 -0.0016 -0.0015 -0.0017 -0.002 -0.0018 -0.0017 -0.003 0.00087 -0.00011 -0.00036 -0.0005 -0.012 0.00062 -0.00048 -0.0029 0.0039 -0.00052 -0.016 0.057 0.0096 0.0068 0.014 0.015 0.0049 -0.018 -0.016 0.049 0.073 0.077 -0.00094 0.066 -1.6e-05 0.026 -0.0054 0.062 -2.2e-05 0.0013 -0.019
Census_OEMNameIdentifier 0.0063 -0.0016 0.0021 -0.0074 -0.0074 -0.0071 -0.0056 -0.028 0.012 0.0031 -0.017 -0.0078 0.003 0.13 -0.0027 0.023 -0.012 0.023 0.044 0.028 0.0085 1.5e-05 0.024 -0.00057 -0.0067 0.01 0.17 0.019 1 0.15 -0.031 0.041 0.098 -0.007 0.027 0.14 0.025 0.13 0.023 0.025 0.024 -0.0067 -0.012 0.025 0.025 0.021 0.011 0.013 -0.0041 0.0042 0.0003 0.15 0.21 -0.12 nan 0.025 0.055 0.0051 0.17 0.0024 0.011 -0.00084 -0.0011 -0.0011 -0.0005 0.00028 -0.00044 0.00064 0.0026 0.0028 0.0024 0.0071 0.0025 0.002 0.0033 0.0015 0.0014 0.0034 0.0011 -0.0023 0.0019 0.0024 0.00079 0.013 0.00019 0.01 0.0014 -0.00064 0.0049 0.0019 -0.00015 0.017 0.0015 0.0012 0.0018 0.0022 0.0024 0.016 -0.017 -0.024 0.0047 0.00049 0.013 0.00092 -0.015 -5.7e-05 0.056 -0.017 -0.051 -9.5e-05 0.015 0.04
Census_OEMModelIdentifier 0.037 -0.00088 -0.0011 -0.0028 -0.002 0.0095 -0.038 -0.013 0.022 0.011 -0.0075 0.0009 0.02 0.063 0.017 0.004 -0.091 0.0021 0.015 0.079 0.017 -0.0009 0.0062 -0.0056 -0.011 0.014 0.056 0.01 0.15 1 -0.015 0.1 0.066 0.007 -0.002 0.052 -0.0015 0.063 -0.015 -0.0084 -0.0026 -0.021 -0.061 -0.014 0.05 0.045 0.011 -0.0002 -0.075 0.0068 0.0017 0.0086 0.29 -0.098 nan -0.028 0.034 -0.0077 -0.014 -0.04 0.078 -0.00064 -0.00087 0.00018 5.6e-05 0.0022 -0.0032 -0.002 -0.00047 5.9e-05 -0.0033 0.002 -0.0022 -0.00084 0.0052 -0.00039 0.00091 -0.0041 -0.00084 -0.0038 -0.0035 -0.0002 0.0014 0.0041 0.001 -0.015 -0.0011 0.0015 -0.0096 -0.0047 0.0017 0.0068 -0.0018 -0.0015 -0.0036 0.0041 -0.0029 -0.0092 0.016 -0.016 0.015 0.0046 0.021 0.0028 -0.017 -5.4e-05 0.043 -0.017 0.0036 -0.00014 0.025 -0.014
Census_ProcessorManufacturerIdentifier 0.0082 0.011 0.011 0.00034 -0.0018 0.0038 -0.0059 -0.021 0.031 -0.019 0.00063 0.011 0.011 -0.011 0.015 0.017 0.033 0.018 0.0043 -0.025 -0.0019 -0.0043 0.0076 -0.0042 0.016 -0.0048 -0.053 -0.0099 -0.031 -0.015 1 0.37 0.026 0.00025 -0.08 -0.029 0.0065 -0.012 0.0086 0.013 0.0061 -0.038 -0.00047 0.014 0.037 -0.0028 -0.0032 0.0015 -0.061 -0.0047 0.00036 0.00033 -0.03 -0.021 nan -0.02 -0.075 -0.058 -0.088 0.023 -0.017 0.00025 0.00063 0.00011 9e-05 -1.2e-05 -0.00043 0.00017 -0.0012 0.0024 -0.0024 0.0021 0.00073 0.0017 0.0049 0.0016 0.0062 0.0018 0.0052 0.0057 0.0004 0.0029 0.0093 -0.0017 5.3e-05 0.0028 0.0028 0.001 -0.0027 0.0013 0.00087 -0.0052 0.0006 -0.00015 -0.0027 0.001 -0.0019 -0.0014 -0.028 0.033 0.0055 0.0055 0.0048 0.0017 -0.0076 -0.00015 -0.023 0.012 0.14 0.00067 -0.057 -0.14
Census_ProcessorModelIdentifier 0.046 0.0063 0.013 -0.0062 -0.0095 -0.002 -0.045 -0.032 0.034 0.00023 -0.033 -0.0069 0.083 0.13 0.05 0.035 -0.13 0.035 0.054 0.14 0.018 0.0051 0.024 0.0077 -0.015 0.015 0.081 0.09 0.041 0.1 0.37 1 0.17 0.028 0.023 0.17 0.015 0.13 -0.014 0.016 0.012 -0.0058 -0.068 -0.035 0.035 0.018 -0.0054 0.056 -0.2 0.016 0.004 0.023 0.11 -0.2 nan 0.084 -0.029 0.0079 -0.033 -0.012 0.032 0.0025 0.0014 0.0004 0.0013 0.0035 0.0044 0.0017 -0.0013 0.0087 -0.0017 0.0084 -0.00086 0.00093 0.0078 0.0027 0.0088 -0.00025 0.0035 -0.0012 -0.003 0.0026 0.0025 -0.0015 0.0037 -0.036 0.0012 0.0046 0.0053 -0.003 0.0044 0.0059 0.0033 -0.0016 -0.0053 0.0081 -0.0017 -0.014 0.013 -0.027 0.036 0.029 0.036 0.007 -0.014 -0.00023 0.071 -0.026 0.1 0.00034 0.04 -0.1
Census_PrimaryDiskTypeName 0.03 -0.018 -0.0018 -0.012 -0.0057 -0.015 -0.029 -0.028 0.033 0.0097 -0.029 -0.0066 -0.0035 0.18 -0.025 0.016 -0.16 0.0076 0.058 0.16 0.048 0.015 0.035 -2.2e-06 -0.0086 0.015 0.24 0.059 0.098 0.066 0.026 0.17 1 -0.0061 0.065 0.33 0.016 0.19 3.3e-05 0.017 0.011 -0.035 -0.095 -0.0016 0.055 0.074 -0.016 0.074 -0.035 0.031 0.0047 0.01 0.11 -0.19 nan 0.15 0.075 0.1 0.19 0.01 0.073 -0.0018 -0.0027 -0.0022 -0.0003 0.00022 0.002 0.00076 0.0013 0.023 0.007 0.015 0.013 0.0021 0.0089 0.0065 0.014 -0.0036 0.0073 -0.00069 0.005 -0.0016 -0.01 0.0041 -0.00099 -0.018 -0.0033 -0.0014 0.015 -0.00022 -0.00099 0.015 6.8e-05 0.0002 0.0034 0.0036 0.0039 0.0046 0.037 -0.08 -0.0022 -0.015 0.024 0.01 -0.033 -0.00025 0.11 -0.039 0.042 -0.00036 0.18 0.11
Census_HasOpticalDiskDrive -0.0049 0.0026 0.0038 0.0029 0.0086 0.0076 0.0051 0.025 -0.0078 -0.0079 0.0042 0.016 -0.0055 0.016 -0.0059 -0.0012 -0.041 -0.0028 -0.0013 0.034 -0.0086 -0.0055 0.0011 0.032 0.00098 -0.0016 -0.057 0.0029 -0.007 0.007 0.00025 0.028 -0.0061 1 -0.027 -0.016 -0.0087 0.016 0.0016 -0.00025 -0.0059 -0.0022 -0.026 -0.087 0.012 0.0096 0.065 -0.03 -0.074 -0.0012 0.0037 -0.014 0.005 -0.06 nan -0.019 -0.057 -0.038 -0.057 0.044 -0.0045 0.00015 -0.00097 -0.00084 -0.0006 -0.00098 0.0082 0.003 0.0014 -0.012 -0.007 -0.0072 -0.0094 -0.0041 -0.0036 -0.0041 -0.00085 -0.014 0.0015 -0.0026 0.00093 -0.0032 -0.0023 -0.0016 -0.00081 -0.021 0.0029 -0.00081 0.014 0.0035 -0.00072 0.0073 -1.9e-05 0.0016 0.00075 4.3e-05 0.00013 -0.0047 -1.6e-06 0.015 -0.0014 -0.0016 -0.0044 0.0026 0.037 -0.00012 -0.027 0.0043 0.015 0.0009 -0.013 -0.055
Census_ChassisTypeName -0.006 0.0069 0.0083 -0.0059 -0.0081 -0.008 0.00077 -0.0053 -0.011 0.0099 0.0018 -0.018 0.036 0.027 -0.0059 0.019 0.0097 0.019 0.032 0.022 0.0027 0.015 0.016 0.0037 -0.0094 -0.0042 0.27 0.11 0.027 -0.002 -0.08 0.023 0.065 -0.027 1 0.15 0.041 0.027 0.03 0.024 0.041 0.051 0.038 0.027 -0.029 -0.034 0.023 0.047 0.051 0.0072 0.00067 0.037 0.055 0.054 nan 0.18 0.27 0.26 0.18 -0.068 0.00087 -0.00021 -0.0016 -0.00032 0.00021 -0.0003 -0.00078 -0.00074 -0.0015 -0.0019 -0.0046 -0.00058 -0.0023 0.0038 -0.0023 0.00015 0.0002 5.3e-06 0.00044 -0.0024 0.002 0.0025 0.0029 0.0095 -0.00075 0.021 0.0014 -0.0012 -0.0016 0.0039 -0.0012 0.022 0.0087 0.004 0.0039 0.0015 0.0047 0.009 -0.037 -0.00032 0.0053 0.0063 0.0071 -0.00096 -0.0021 -0.00012 0.036 -0.0098 -0.036 -0.00017 0.088 0.22
Census_PowerPlatformRoleName 0.014 -0.022 -0.022 -0.013 -0.011 -0.013 -0.013 -0.034 0.027 0.012 -0.026 -0.0069 0.023 0.22 -0.019 -0.012 -0.16 -0.02 0.053 0.15 0.042 -0.0038 0.0026 0.0068 -0.046 0.021 0.65 0.12 0.14 0.052 -0.029 0.17 0.33 -0.016 0.15 1 -0.0034 0.22 -0.032 -0.012 -0.0067 -0.0069 -0.092 -0.0047 0.021 0.025 0.026 0.055 -0.032 0.0097 0.0022 0.00028 0.083 -0.2 nan 0.037 0.17 0.17 0.46 0.026 0.049 -0.0015 -0.0024 -0.0023 -0.00083 -0.00053 -0.0069 -0.0019 -0.00039 0.0021 0.00097 0.0026 0.0015 0.0022 0.0015 -0.0054 0.0039 -0.0025 -0.0032 -0.009 -0.00047 0.00032 -0.0088 0.0098 -0.0012 -0.024 -0.0057 -0.0013 -0.008 -0.0043 -0.0018 0.0028 0.002 -0.00038 -0.0003 0.0012 0.0031 0.012 0.069 -0.086 0.0029 -0.0025 0.025 0.0062 -0.028 -0.00022 0.11 -0.039 -0.034 -0.00031 0.2 0.011
Census_OSVersion 0.092 0.14 0.37 -0.0098 -0.021 0.016 -0.093 -0.071 0.015 -0.005 -0.039 -0.031 0.19 0.053 0.14 0.55 0.075 0.53 0.59 0.04 -0.11 -0.0077 0.52 -0.001 -0.0041 0.015 0.059 0.13 0.025 -0.0015 0.0065 0.015 0.016 -0.0087 0.041 -0.0034 1 0.054 0.48 0.57 0.97 0.082 0.094 0.14 -0.03 -0.039 0.037 0.022 0.036 0.14 0.024 0.037 0.0024 0.053 nan 0.021 0.042 -0.00057 0.066 -0.12 0.036 0.0062 0.0041 0.006 0.0038 0.0059 0.023 0.12 0.071 0.045 0.095 0.047 -0.0046 0.033 0.026 0.02 0.061 -0.0034 0.05 0.1 0.16 0.066 0.1 0.043 0.011 0.25 0.13 0.01 0.082 0.13 0.013 0.075 0.08 0.092 0.046 0.034 0.07 0.043 -0.5 -0.0051 0.12 0.092 0.19 0.11 -0.0057 -0.00029 -0.02 -0.0023 -0.07 0.00022 -0.083 0.026
Census_OSArchitecture 0.072 0.034 0.071 -0.012 -0.013 -0.0043 -0.066 -0.0078 -0.0022 -0.0081 -0.028 -0.0007 0.0035 0.99 0.0092 0.09 -0.09 0.096 0.21 0.076 -0.0099 -0.0041 0.11 -0.031 0.013 0.011 0.23 -0.013 0.13 0.063 -0.012 0.13 0.19 0.016 0.027 0.22 0.054 1 0.11 0.085 0.056 -0.065 -0.094 0.028 0.015 -0.01 0.044 0.015 -0.12 0.0037 0.0014 0.078 0.094 -0.23 nan -0.006 0.076 -0.023 0.3 -0.043 -0.0086 0.0009 -0.0012 -0.00082 -0.00035 0.0051 0.0022 0.0049 0.0036 0.0095 0.011 0.01 0.0052 0.0069 0.016 0.0075 0.0065 -0.0039 0.011 0.0055 0.013 0.0043 0.005 0.0048 0.0023 -0.01 0.0071 0.004 0.038 0.013 0.0022 0.15 0.004 0.017 0.012 0.014 0.011 0.046 -0.074 -0.13 0.014 -0.01 0.023 -0.0024 -0.052 -0.00013 0.05 -0.021 -0.19 -0.00018 -0.11 0.0089
Census_OSBranch 0.037 0.11 0.32 -0.02 -0.039 0.0082 -0.037 -0.076 0.0074 -0.0083 -0.041 -0.038 0.051 0.11 0.057 0.56 0.063 0.56 0.28 -0.022 -0.12 -0.016 0.57 -0.027 0.0097 -0.0024 0.032 -0.016 0.023 -0.015 0.0086 -0.014 3.3e-05 0.0016 0.03 -0.032 0.48 0.11 1 0.61 0.46 0.028 0.055 0.18 -0.035 -0.054 0.05 0.053 0.067 0.088 0.016 0.036 -0.012 0.054 nan -0.0057 0.041 -0.011 0.082 -0.1 0.024 0.0034 0.0017 0.0021 0.0017 0.0024 0.11 0.053 0.026 0.03 0.02 0.022 0.027 0.013 0.021 0.017 0.025 -0.036 0.014 0.00061 0.088 -0.015 -0.032 -0.0038 0.0032 0.49 0.018 0.0036 0.2 0.037 0.0049 0.43 -0.018 -0.024 -0.022 0.0057 -0.015 -0.067 -0.72 -0.00087 0.047 0.03 0.085 0.067 -0.012 -0.00033 -0.026 0.0022 -0.11 -0.00012 -0.11 0.035
Census_OSBuildNumber 0.15 0.11 0.39 -0.02 -0.035 0.018 -0.14 -0.064 0.01 -0.01 -0.057 -0.037 0.23 0.085 0.21 0.86 0.041 0.78 0.56 0.063 -0.11 -0.013 0.66 -0.01 0.00042 0.02 0.043 0.047 0.025 -0.0084 0.013 0.016 0.017 -0.00025 0.024 -0.012 0.57 0.085 0.61 1 0.51 0.065 0.08 0.14 -0.047 -0.058 -0.043 0.074 0.052 0.26 0.054 0.032 -0.0039 0.017 nan 0.013 0.034 -0.02 0.073 -0.11 0.039 0.009 0.007 0.0078 0.0055 0.011 0.14 0.059 0.029 0.096 0.068 0.065 0.087 0.05 0.066 0.062 0.069 0.086 0.068 0.067 0.11 0.06 0.079 0.0095 0.018 0.16 0.07 0.017 0.35 0.1 0.017 0.075 0.045 0.061 0.059 0.035 0.055 0.19 -0.58 -0.032 0.15 0.11 0.15 0.14 0.00073 -0.00028 -0.012 -0.0071 -0.097 0.00027 -0.11 0.011
Census_OSBuildRevision 0.094 0.14 0.36 -0.0094 -0.02 0.018 -0.094 -0.07 0.015 -0.0057 -0.036 -0.027 0.19 0.056 0.14 0.5 0.085 0.52 0.56 0.034 -0.11 -0.008 0.47 -0.00086 -0.0023 0.015 0.058 0.13 0.024 -0.0026 0.0061 0.012 0.011 -0.0059 0.041 -0.0067 0.97 0.056 0.46 0.51 1 0.083 0.1 0.15 -0.022 -0.036 0.056 0.0051 0.041 0.095 0.014 0.039 0.00019 0.064 nan 0.018 0.045 0.0002 0.069 -0.12 0.036 0.0062 0.0042 0.006 0.0039 0.0055 0.026 0.12 0.065 -0.034 0.099 0.047 -0.005 0.034 0.028 0.022 0.062 -0.0038 0.053 0.1 0.17 0.065 0.1 0.0068 0.011 0.23 0.14 0.01 0.092 0.14 0.013 0.089 0.083 0.095 0.047 0.034 0.072 0.049 -0.5 0.0042 0.12 0.09 0.18 0.024 -0.0054 -0.0003 -0.028 -1.2e-06 -0.073 0.00024 -0.085 0.029
Census_OSEdition -0.042 -0.0052 0.05 0.0034 -7.7e-05 -0.0057 0.033 -0.12 -0.069 0.013 -0.034 -0.077 0.1 -0.065 -0.0095 0.048 -0.14 0.053 0.073 0.44 -0.025 -0.0084 0.039 0.073 -0.048 0.013 0.011 0.31 -0.0067 -0.021 -0.038 -0.0058 -0.035 -0.0022 0.051 -0.0069 0.082 -0.065 0.028 0.065 0.083 1 0.67 -0.0052 -0.063 -0.14 -0.048 0.055 0.11 0.041 4.9e-05 -0.0059 -0.0088 0.033 nan 0.088 -0.065 -0.0014 -0.064 0.027 -0.15 0.0011 0.0015 0.0024 0.0011 -0.0014 0.051 0.0045 0.003 -0.0057 0.00026 -0.003 0.002 -0.00077 -0.0081 -0.0059 -0.0047 0.0035 -0.0073 -0.0025 -0.0073 -0.0021 -0.0056 -0.0024 2e-05 -0.011 -0.0038 -0.0016 0.07 -0.0078 0.00089 -0.019 0.012 -3.7e-05 -0.0039 0.0044 0.0049 -0.0016 -0.038 0.0082 0.03 0.038 0.062 0.029 0.072 -0.00033 0.011 0.001 -0.0097 -0.00046 -0.026 -0.02
Census_OSSkuName -0.037 0.014 0.039 0.01 0.0097 0.012 0.028 -0.084 -0.026 -0.00045 0.012 -0.027 0.14 -0.093 0.026 0.072 0.5 0.086 0.07 -0.09 -0.027 -0.0061 0.04 0.039 -0.0043 -0.013 0.022 0.32 -0.012 -0.061 -0.00047 -0.068 -0.095 -0.026 0.038 -0.092 0.094 -0.094 0.055 0.08 0.1 0.67 1 0.078 -0.0015 -0.06 0.043 -0.073 0.17 0.0056 -0.0044 0.0023 -0.045 0.26 nan 0.056 0.0058 -0.036 -0.0094 0.014 -0.072 0.0033 0.0044 0.0047 0.0024 0.0003 0.023 -0.003 0.0045 -0.019 -0.0044 -0.008 0.0031 0.0042 -0.0018 -0.0043 -0.014 -0.0052 0.0056 0.006 0.01 0.00075 0.0065 -0.0012 0.0017 0.026 0.0063 -0.00024 0.048 0.011 0.002 -0.02 0.022 0.0094 0.0074 0.005 0.013 0.028 -0.09 0.047 0.031 0.049 0.031 0.0015 0.029 -7.5e-06 -0.084 0.036 -0.031 -1e-05 -0.057 0.036
Census_OSInstallTypeName 0.011 0.037 0.067 0.0006 -0.0025 -0.0074 -0.014 -0.15 0.031 0.0016 -0.034 -0.043 0.011 0.028 0.019 0.12 0.17 0.15 0.089 -0.15 -0.033 0.012 0.11 -0.061 0.024 -0.0088 0.055 -0.021 0.025 -0.014 0.014 -0.035 -0.0016 -0.087 0.027 -0.0047 0.14 0.028 0.18 0.14 0.15 -0.0052 0.078 1 0.019 -0.0028 -0.018 -0.048 0.084 -0.049 -0.0058 0.025 -0.0013 0.16 nan -0.036 0.049 0.012 0.098 0.015 0.00089 0.0012 0.00074 0.0014 0.00088 0.0016 -0.023 0.00026 0.0085 0.0017 0.013 0.0042 0.024 0.016 0.013 0.0098 -0.0034 0.019 0.022 0.026 0.04 0.01 0.023 0.005 0.0014 0.15 0.02 0.0016 0.0024 0.035 0.0012 0.03 0.015 0.023 0.025 0.0037 0.021 0.081 -0.18 0.0092 0.003 0.0047 0.0093 -0.021 -0.094 0.00067 0.013 0.00018 -0.053 -0.0003 -0.03 0.072
Census_OSInstallLanguageIdentifier 0.0048 -0.0041 -0.023 0.0045 0.0068 0.023 -0.0046 -0.061 0.044 -0.0024 0.26 0.28 0.0025 0.015 0.0071 -0.037 0.055 -0.039 -0.025 -0.057 0.046 -0.0023 -0.038 0.0043 0.03 -0.00065 -0.0012 -0.011 0.025 0.05 0.037 0.035 0.055 0.012 -0.029 0.021 -0.03 0.015 -0.035 -0.047 -0.022 -0.063 -0.0015 0.019 1 0.67 0.044 -0.058 -0.062 -0.013 -0.0011 -0.002 0.052 -0.00047 nan -0.011 -0.054 -0.019 -0.0057 -0.0069 -0.037 -0.00045 0.00032 -4.3e-05 0.00059 -0.00026 -0.031 -0.0087 -0.0035 -0.048 -0.025 -0.0032 -0.006 -0.0046 -0.0018 -0.0087 -0.016 -0.011 -0.0027 -0.0056 -0.0042 -0.002 0.00041 -0.00068 -0.00071 -0.0098 -0.00098 0.00032 -0.037 -0.0042 -1.4e-05 -0.0016 0.00064 0.00084 0.00059 0.0013 0.00045 -0.00085 0.021 0.023 0.0017 0.0036 0.0036 -0.00025 0.0013 -0.00017 -0.022 0.0084 0.053 -0.00049 0.028 -0.018
Census_OSUILocaleIdentifier 0.014 -0.013 -0.034 0.00037 0.0029 0.017 -0.013 0.0068 0.08 0.011 0.11 0.31 -0.001 -0.0099 0.0043 -0.048 0.033 -0.05 -0.031 -0.039 0.046 0.0005 -0.041 -0.0049 0.028 0.0017 -0.0076 -0.013 0.021 0.045 -0.0028 0.018 0.074 0.0096 -0.034 0.025 -0.039 -0.01 -0.054 -0.058 -0.036 -0.14 -0.06 -0.0028 0.67 1 0.036 -0.044 -0.038 -0.014 -0.00078 0.014 0.051 -0.015 nan -0.011 -0.05 -0.014 0.0041 -0.013 0.056 -0.00075 0.0002 -0.0016 -8.2e-05 -0.00022 -0.029 -0.01 -0.0044 -0.0085 -0.014 -0.0041 -0.0067 -0.0053 -0.0012 -0.0078 -0.015 -0.014 -0.0032 -0.0044 -0.0059 -0.0041 -0.0031 -0.00041 -0.00093 -0.021 -0.003 0.0002 -0.041 -0.0058 -0.00051 -0.0097 -0.0021 -0.0028 -0.0016 0.0013 -0.0011 -0.012 0.052 0.0009 0.0026 -0.0015 0.0024 -0.0017 -0.014 0.0003 0.0091 0.0016 0.038 -0.00042 0.041 -0.016
Census_OSWUAutoUpdateOptionsName 0.008 -0.0029 -0.037 0.0088 0.019 0.033 -0.0092 -0.0066 0.027 -0.00042 0.028 0.046 0.072 0.043 0.004 -0.033 0.13 -0.035 -0.0056 -0.083 0.013 -0.008 -0.021 0.039 -0.00047 -0.0018 0.058 0.18 0.011 0.011 -0.0032 -0.0054 -0.016 0.065 0.023 0.026 0.037 0.044 0.05 -0.043 0.056 -0.048 0.043 -0.018 0.044 0.036 1 -0.23 -0.036 -0.11 0.0025 0.025 0.02 0.1 nan 0.00051 0.046 0.037 0.058 0.0011 0.06 0.0011 0.0008 0.00091 0.00072 0.00016 -0.076 -0.032 -0.014 -0.064 -0.044 -0.034 -0.052 -0.022 -0.019 -0.028 -0.031 -0.075 -0.015 -0.019 -0.036 -0.026 -0.027 0.0014 -0.00026 0.14 0.021 0.00026 -0.16 0.041 6.1e-05 0.011 0.027 0.02 0.021 0.0049 0.016 0.019 -0.074 0.1 0.0095 0.014 0.00057 -0.0071 0.022 -2.1e-05 -0.13 0.044 0.01 -3.3e-05 0.0083 0.034
Census_GenuineStateName -0.012 0.0075 0.054 -0.0086 -0.024 -0.021 0.0075 -0.0053 -0.028 0.0076 -0.062 -0.053 -0.0043 0.015 -0.0069 0.058 -0.21 0.063 0.022 0.19 -0.0063 0.0016 0.05 -0.0098 -0.021 0.0067 -0.02 0.0079 0.013 -0.0002 0.0015 0.056 0.074 -0.03 0.047 0.055 0.022 0.015 0.053 0.074 0.0051 0.055 -0.073 -0.048 -0.058 -0.044 -0.23 1 -0.11 0.13 -0.0017 -0.0031 0.0082 -0.2 nan 0.15 -0.073 -0.034 -0.055 -0.043 -0.059 -0.00043 -0.0013 -0.00078 -0.00087 0.00083 0.077 0.016 0.00089 0.069 0.028 0.027 0.02 -0.0031 -0.0039 0.001 0.014 0.06 -0.0065 -0.0051 -0.0086 0.0076 -0.011 0.0051 0.00029 -0.0051 -0.0092 -0.0011 0.11 -0.016 0.00018 0.01 -0.0061 -0.011 -0.011 -0.0014 -0.0073 -0.015 -0.013 -0.065 -0.0052 -0.00094 -0.0034 0.00096 -0.013 -0.00014 0.12 -0.034 -0.0025 -0.00019 -0.031 -0.066
Census_ActivationChannel -0.093 -0.0094 0.02 0.0077 0.013 -0.0043 0.091 0.047 -0.028 0.011 0.05 0.00064 -0.14 -0.12 -0.15 -0.008 0.16 -0.011 -0.018 -0.081 -0.029 0.0063 0.016 0.019 -0.015 -0.015 0.082 0.023 -0.0041 -0.075 -0.061 -0.2 -0.035 -0.074 0.051 -0.032 0.036 -0.12 0.067 0.052 0.041 0.11 0.17 0.084 -0.062 -0.038 -0.036 -0.11 1 0.037 -0.0073 0.0043 -0.046 0.34 nan -0.027 0.087 0.069 0.15 0.037 -0.029 -0.0077 -0.0057 -0.0073 -0.0045 -0.0071 0.06 0.031 0.012 -0.0097 0.0071 -0.0065 0.013 0.0052 -0.009 0.0044 0.00087 0.0032 -0.0027 0.0068 0.0066 -0.00025 -0.003 0.0042 -0.011 0.068 -0.0046 -0.011 0.13 -0.0015 -0.011 -0.037 -0.0021 0.00029 0.005 -0.014 0.0039 0.023 -0.024 -0.014 -0.081 -0.068 -0.041 -0.0012 0.049 -0.00036 -0.12 0.058 -0.016 -0.00051 0.08 0.2
Census_FlightRing -0.019 0.019 0.075 0.00013 -0.0036 -0.002 0.017 -0.022 0.006 -0.0011 -0.033 -0.026 -0.028 0.004 -0.025 0.25 -0.057 0.13 0.13 0.08 -0.021 0.0084 0.2 0.013 -0.0023 -0.0011 0.0051 -0.0087 0.0042 0.0068 -0.0047 0.016 0.031 -0.0012 0.0072 0.0097 0.14 0.0037 0.088 0.26 0.095 0.041 0.0056 -0.049 -0.013 -0.014 -0.11 0.13 0.037 1 0.068 -0.00049 0.011 -0.043 nan 0.026 -0.0008 0.0053 0.0024 -0.033 0.0072 -0.0016 -0.0013 -0.001 -0.00057 -0.0011 0.12 0.05 0.022 -0.0094 -0.0065 -0.0049 -0.0078 -0.0058 -0.0044 -0.0068 -0.0046 -0.0093 -0.0038 -0.0042 -0.012 -0.00073 -0.013 0.0017 -0.0022 -0.048 -0.0056 -0.0021 0.25 -0.016 -0.0013 -0.051 -0.0083 -0.01 -0.01 -0.0028 -0.0076 -0.031 1.6e-05 -0.054 0.025 -0.00051 0.086 0.17 0.015 -9.1e-05 0.036 -0.012 0.00046 -0.00013 0.01 0.0064
Census_ThresholdOptIn 0.0006 0.0011 0.0024 -0.00053 -0.00054 9.8e-05 -0.00044 -0.002 0.0028 0.00011 -0.0028 -0.0021 0.0013 0.0013 0.0014 0.037 -0.0075 0.013 0.023 0.0056 0.00058 -0.0002 0.029 0.0029 -5.2e-05 0.0005 0.0012 -0.00039 0.0003 0.0017 0.00036 0.004 0.0047 0.0037 0.00067 0.0022 0.024 0.0014 0.016 0.054 0.014 4.9e-05 -0.0044 -0.0058 -0.0011 -0.00078 0.0025 -0.0017 -0.0073 0.068 1 -0.00014 0.0012 -0.0057 nan 0.0047 0.0012 0.0019 -0.00026 4.3e-06 0.0049 -8.3e-05 -6.1e-05 -7.8e-05 -4.8e-05 -7.7e-05 -8.2e-05 -0.00026 -0.00013 -0.00058 3.9e-06 -0.00039 -0.00052 0.00023 -0.00039 6.8e-05 0.0004 -3.6e-05 0.00028 -0.00016 -7.6e-05 -0.00021 0.00014 -8.8e-05 -0.00012 -0.0012 -0.00027 -0.00012 -0.00044 2.1e-06 -0.00013 -0.0018 -0.00042 -0.00027 -0.00054 -0.00025 0.0002 -0.001 -0.0043 -0.0014 0.013 0.0029 0.017 0.021 0.0029 -3.9e-06 0.003 -0.00083 0.003 -5.5e-06 0.0028 0.00015
Census_FirmwareManufacturerIdentifier 0.012 0.011 0.015 -0.0056 -0.0073 -0.00056 -0.01 0.0075 0.0037 -0.00078 0.0097 0.0078 0.013 0.078 0.0012 0.03 0.02 0.033 0.037 -0.0048 -0.01 0.007 0.029 -0.0057 0.0061 -0.001 0.037 0.036 0.15 0.0086 0.00033 0.023 0.01 -0.014 0.037 0.00028 0.037 0.078 0.036 0.032 0.039 -0.0059 0.0023 0.025 -0.002 0.014 0.025 -0.0031 0.0043 -0.00049 -0.00014 1 0.065 -0.0012 nan 0.045 0.053 0.049 0.078 -0.029 0.0077 -0.0015 -0.0024 -0.0023 -0.00028 -0.00042 0.0027 0.00036 0.0016 -0.00088 0.0054 0.0048 -0.0013 0.0026 0.0057 0.0033 0.0011 -0.003 0.0067 0.01 0.00013 0.0025 0.004 -0.00063 -0.00063 0.0088 0.0042 -3e-05 0.01 0.0073 -8.2e-05 0.028 0.0038 0.0051 0.0039 0.0027 0.0036 0.01 -0.043 0.0025 0.0042 -0.00016 0.0047 -0.0027 -0.0063 -0.00017 -0.002 -0.00044 -0.027 0.00014 -0.016 0.052
Census_FirmwareVersionIdentifier 0.024 -0.007 -0.0075 -0.0049 -0.0037 0.00015 -0.023 -0.035 0.017 0.013 -0.029 -0.016 0.019 0.093 0.0066 0.0035 -0.082 -0.0011 0.027 0.08 0.021 0.00037 0.0064 0.0017 -0.015 0.019 0.099 0.034 0.21 0.29 -0.03 0.11 0.11 0.005 0.055 0.083 0.0024 0.094 -0.012 -0.0039 0.00019 -0.0088 -0.045 -0.0013 0.052 0.051 0.02 0.0082 -0.046 0.011 0.0012 0.065 1 -0.12 nan -0.016 0.044 0.011 0.06 -0.0091 0.054 -7.8e-05 0.00071 0.00011 8.8e-05 0.0012 -0.0038 -0.0014 -0.00033 0.0013 -0.00058 0.0041 -0.00073 -0.0012 0.0021 -0.00093 0.00047 -0.0033 -0.00018 -0.0073 -0.00068 -7.2e-05 -0.00044 0.0047 0.00045 -0.011 -0.0021 0.001 -0.01 -0.0047 0.0012 0.0066 -0.0015 -0.0022 -0.0019 0.0035 -0.0017 -0.0024 0.022 -0.029 0.011 0.0032 0.019 0.0049 -0.017 7.7e-05 0.068 -0.022 -0.0047 -0.00024 0.062 0.058
Census_IsSecureBootEnabled -0.093 0.02 0.0085 0.017 0.02 0.023 0.086 0.019 -0.0076 -0.0024 0.11 0.044 0.024 -0.23 0.034 0.0057 0.45 0.02 -0.03 -0.4 -0.038 0.017 -0.023 -0.026 0.031 -0.035 -0.019 -0.032 -0.12 -0.098 -0.021 -0.2 -0.19 -0.06 0.054 -0.2 0.053 -0.23 0.054 0.017 0.064 0.033 0.26 0.16 -0.00047 -0.015 0.1 -0.2 0.34 -0.043 -0.0057 -0.0012 -0.12 1 nan -0.072 0.16 0.12 0.067 -0.025 -0.029 0.0035 0.0049 0.0051 0.0019 -0.003 -0.044 -0.0097 0.0014 -0.029 -0.0063 -0.016 0.00059 0.0033 -0.01 -0.0019 -0.016 -0.017 0.0093 0.013 0.015 -0.0021 0.011 -0.0042 0.00086 0.07 0.0094 -0.0024 -0.051 0.02 0.00018 -0.0014 0.0081 0.0099 0.012 -0.004 0.012 0.032 -0.1 0.099 0.00058 0.032 -0.033 -0.011 -0.0076 0.00041 -0.21 0.08 -0.0091 9.3e-06 0.033 0.22
Census_IsWIMBootEnabled nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Census_IsVirtualDevice -0.0044 -0.0025 -0.0026 -0.0079 -0.01 -0.0082 0.0021 -0.0096 0.0014 0.016 -0.02 -0.018 0.061 -0.006 -0.011 0.01 -0.038 0.0035 0.026 0.1 0.012 -0.0013 0.011 0.022 -0.043 0.0049 0.049 0.2 0.025 -0.028 -0.02 0.084 0.15 -0.019 0.18 0.037 0.021 -0.006 -0.0057 0.013 0.018 0.088 0.056 -0.036 -0.011 -0.011 0.00051 0.15 -0.027 0.026 0.0047 0.045 -0.016 -0.072 nan 1 -0.024 0.037 -0.021 -0.044 0.023 -0.00026 -0.00053 -0.00068 -0.00042 -0.00017 0.005 -0.00055 -0.00085 0.0064 -0.0022 0.0043 0.001 -0.0021 -0.0021 -0.0015 0.0011 0.0047 -0.0025 -0.0025 -0.0036 -0.00057 -0.0028 -0.00055 -0.00029 -0.0078 -0.0017 9.8e-05 0.01 -0.0028 -0.00065 -0.0044 0.013 -0.00083 -0.0014 0.0016 0.0015 -0.0028 0.01 -0.018 0.0062 0.0096 0.017 0.0077 -0.0019 -3.4e-05 0.05 -0.011 0.08 -5e-05 0.04 -0.0036
Census_IsTouchEnabled -0.03 0.0015 0.0054 -0.0087 -0.0061 -0.0086 0.029 0.031 -0.0084 0.0058 0.022 -0.0079 -0.0012 0.076 0.0015 0.026 0.091 0.028 0.043 -0.083 -0.0018 0.016 0.022 -0.014 0.016 -0.011 0.51 -0.0099 0.055 0.034 -0.075 -0.029 0.075 -0.057 0.27 0.17 0.042 0.076 0.041 0.034 0.045 -0.065 0.0058 0.049 -0.054 -0.05 0.046 -0.073 0.087 -0.0008 0.0012 0.053 0.044 0.16 nan -0.024 1 0.46 0.45 -0.05 0.054 0.00053 0.00059 0.00098 0.00054 -0.0013 -0.016 -0.0004 0.0024 -0.0069 0.0027 -0.00085 -5e-06 0.0066 -0.0011 0.0017 -0.0016 -0.0057 0.0075 0.0048 0.01 0.0028 0.0019 0.0069 -0.0011 0.014 0.0059 -0.0011 -0.003 0.013 -0.00043 0.035 0.0061 0.0079 0.0087 -0.00079 0.011 0.027 -0.042 -0.0082 0.00049 0.005 -0.0097 -0.00089 -0.014 -0.00015 0.011 -0.0087 -0.07 -0.00022 0.18 0.45
Census_IsPenCapable -0.017 -0.012 -0.022 -0.0054 -0.0013 -0.0079 0.017 0.0011 2.9e-05 0.013 0.0058 -0.011 -0.013 -0.023 -0.014 -0.024 -0.053 -0.029 -0.012 0.037 0.012 0.024 -0.018 -0.0078 0.0098 -0.0057 0.33 0.0015 0.0051 -0.0077 -0.058 0.0079 0.1 -0.038 0.26 0.17 -0.00057 -0.023 -0.011 -0.02 0.0002 -0.0014 -0.036 0.012 -0.019 -0.014 0.037 -0.034 0.069 0.0053 0.0019 0.049 0.011 0.12 nan 0.037 0.46 1 0.38 -0.019 0.058 -0.0007 -0.00086 -0.00025 0.00035 -0.0014 -0.011 -0.002 -0.0018 -0.0086 -0.0019 -0.0038 -0.0065 -0.0035 -0.004 -0.0026 -0.0041 -0.0081 -0.0031 -0.0032 -0.0036 -0.0023 -0.0033 -0.00068 -0.001 0.0025 -0.002 -0.0014 -0.018 -0.0015 -0.0013 0.0046 0.0011 1.7e-05 -0.00078 -0.0018 0.00084 -0.0033 0.017 -0.0037 -0.0061 -0.0055 -0.0066 0.0017 -0.012 -8e-05 0.029 -0.0092 -0.012 -0.00012 0.3 0.44
Census_IsAlwaysOnAlwaysConnectedCapable -0.025 0.0016 0.015 -0.018 -0.016 -0.018 0.026 -0.014 0.0074 0.0037 -0.004 -0.0042 -0.024 0.3 -0.02 0.057 0.056 0.06 0.1 -0.057 0.0048 0.015 0.061 -0.026 0.019 -0.0097 0.66 -0.0098 0.17 -0.014 -0.088 -0.033 0.19 -0.057 0.18 0.46 0.066 0.3 0.082 0.073 0.069 -0.064 -0.0094 0.098 -0.0057 0.0041 0.058 -0.055 0.15 0.0024 -0.00026 0.078 0.06 0.067 nan -0.021 0.45 0.38 1 -0.031 0.051 -0.0011 -0.0011 -0.00095 -0.00067 -0.0017 -0.014 0.0018 0.0042 -0.0028 0.0078 0.0018 0.0091 0.011 0.0046 0.0003 -0.00087 0.00015 0.0081 0.0029 0.022 0.0073 0.0032 0.017 -0.0017 0.028 0.0072 -0.002 0.012 0.021 -0.0017 0.062 0.0085 0.015 0.018 0.00063 0.019 0.067 -0.064 -0.05 -0.01 -0.0096 -0.013 -0.0016 -0.041 -9.8e-05 0.07 -0.021 -0.2 -0.00015 0.16 0.47
Wdft_IsGamer -0.062 -0.084 -0.12 0.01 0.031 -0.0057 0.062 0.021 -0.0086 -0.0054 0.0075 -0.0092 -0.1 -0.043 -0.093 -0.13 -0.017 -0.15 -0.091 -0.00042 0.034 -0.0019 -0.099 0.016 0.0057 -0.0077 -0.018 -0.024 0.0024 -0.04 0.023 -0.012 0.01 0.044 -0.068 0.026 -0.12 -0.043 -0.1 -0.11 -0.12 0.027 0.014 0.015 -0.0069 -0.013 0.0011 -0.043 0.037 -0.033 4.3e-06 -0.029 -0.0091 -0.025 nan -0.044 -0.05 -0.019 -0.031 1 0.0017 -0.0042 -0.0031 -0.0041 -0.0025 -0.0044 0.00077 0.00046 -0.00022 -0.006 -0.0039 -0.0076 -0.0085 -0.007 -0.013 -0.0084 -0.011 -0.019 -0.012 -0.012 -0.019 -0.013 -0.02 -0.00097 -0.0075 -0.027 -0.017 -0.0071 -0.014 -0.023 -0.0074 -0.081 -0.015 -0.015 -0.015 -0.013 -0.016 -0.023 0.14 0.0034 -0.054 -0.046 -0.016 0.011 -0.011 -0.00025 -0.018 0.022 0.067 -0.00035 0.08 -0.035
Wdft_RegionIdentifier 0.047 0.001 0.0067 -0.007 0.0014 0.015 -0.043 -0.005 0.2 0.0022 -0.055 -0.096 0.033 -0.0089 0.028 0.054 -0.028 0.051 0.041 0.044 0.0076 0.01 0.043 -0.029 0.0088 0.0089 0.034 0.014 0.011 0.078 -0.017 0.032 0.073 -0.0045 0.00087 0.049 0.036 -0.0086 0.024 0.039 0.036 -0.15 -0.072 0.00089 -0.037 0.056 0.06 -0.059 -0.029 0.0072 0.0049 0.0077 0.054 -0.029 nan 0.023 0.054 0.058 0.051 0.0017 1 0.00056 0.0011 -0.00095 0.00028 0.0012 0.0047 0.0015 0.00035 -0.0013 0.0019 0.0012 0.00016 0.0038 0.0077 0.0037 0.0017 -0.0047 0.0066 0.0032 0.013 0.0024 0.011 0.00069 0.0016 -0.0039 0.0059 0.0017 0.0036 0.0073 0.0012 0.0037 0.0039 0.0042 0.0021 0.0043 0.004 -0.0015 -0.035 0.015 0.023 0.0077 0.03 0.01 -0.073 -0.00045 0.024 -0.0076 0.034 0.00033 0.1 0.046
Interaction_MYYear_14Month_3 0.017 0.0043 0.048 -0.00092 -0.0011 0.0073 -0.018 -0.0029 -2.4e-05 -6.9e-05 -0.0018 -0.0011 0.044 0.0009 0.044 0.023 0.0038 0.029 0.068 -6e-05 -0.03 -0.00018 0.099 -0.0013 -0.00084 0.0046 -0.0012 -0.00034 -0.00084 -0.00064 0.00025 0.0025 -0.0018 0.00015 -0.00021 -0.0015 0.0062 0.0009 0.0034 0.009 0.0062 0.0011 0.0033 0.0012 -0.00045 -0.00075 0.0011 -0.00043 -0.0077 -0.0016 -8.3e-05 -0.0015 -7.8e-05 0.0035 nan -0.00026 0.00053 -0.0007 -0.0011 -0.0042 0.00056 1 -5.4e-05 -6.8e-05 -4.2e-05 -6.7e-05 -0.00053 -0.00023 -0.00011 -0.00051 -0.00035 -0.00034 -0.00045 -0.00026 -0.00034 -0.00032 -0.00035 -0.00072 -0.00039 -0.00043 -0.00064 -0.00045 -0.00064 -7.7e-05 -0.00011 -0.0027 -0.00049 -0.0001 -0.0014 -0.0007 -0.00011 -0.0037 -0.00037 -0.00049 -0.00047 -0.00022 -0.00042 -0.0015 -0.0076 -0.0035 -0.00088 -0.00072 -0.001 -0.0002 -0.0017 -3.4e-06 0.0033 -0.0012 -0.0014 -4.8e-06 -0.00016 0.00089
Interaction_MYYear_14Month_7 0.0016 0.0043 0.036 -0.00091 -0.00084 0.0077 -0.0042 -0.0023 0.00048 3.7e-06 -0.00098 -0.00074 0.036 -0.0012 0.036 0.016 0.0045 0.021 0.044 -0.0035 -0.025 -0.00013 0.064 -0.00069 -0.00048 0.00079 -0.0016 -0.00025 -0.0011 -0.00087 0.00063 0.0014 -0.0027 -0.00097 -0.0016 -0.0024 0.0041 -0.0012 0.0017 0.007 0.0042 0.0015 0.0044 0.00074 0.00032 0.0002 0.0008 -0.0013 -0.0057 -0.0013 -6.1e-05 -0.0024 0.00071 0.0049 nan -0.00053 0.00059 -0.00086 -0.0011 -0.0031 0.0011 -5.4e-05 1 -5.1e-05 -3.1e-05 -5e-05 -0.0004 -0.00017 -8.5e-05 -0.00038 -0.00026 -0.00025 -0.00034 -0.0002 -0.00025 -0.00024 -0.00026 -0.00053 -0.00029 -0.00032 -0.00048 -0.00034 -0.00048 -5.7e-05 -7.9e-05 -0.002 -0.00036 -7.6e-05 -0.001 -0.00052 -8.2e-05 -0.0028 -0.00027 -0.00036 -0.00035 -0.00016 -0.00031 -0.0011 -0.0057 -0.0026 -0.00065 -0.00054 -0.00076 -0.00015 -0.0014 -2.5e-06 0.0019 -0.00086 -0.00074 -3.4e-06 -0.00046 0.00046
Interaction_MYYear_14Month_10 -0.00083 0.0043 0.051 -0.001 -0.0011 0.0058 -0.0035 -0.0032 -8.3e-05 0.00026 -0.0015 -0.0017 0.047 -0.00083 0.047 0.02 0.0044 0.026 0.065 -0.004 -0.032 -0.00017 0.061 -0.00091 -0.00061 -0.00012 -0.00093 -0.00032 -0.0011 0.00018 0.00011 0.0004 -0.0022 -0.00084 -0.00032 -0.0023 0.006 -0.00082 0.0021 0.0078 0.006 0.0024 0.0047 0.0014 -4.3e-05 -0.0016 0.00091 -0.00078 -0.0073 -0.001 -7.8e-05 -0.0023 0.00011 0.0051 nan -0.00068 0.00098 -0.00025 -0.00095 -0.0041 -0.00095 -6.8e-05 -5.1e-05 1 -4e-05 -6.3e-05 -0.0005 -0.00022 -0.00011 -0.00048 -0.00033 -0.00032 -0.00043 -0.00025 -0.00032 -0.0003 -0.00033 -0.00068 -0.00037 -0.00041 -0.00061 -0.00043 -0.00061 -7.3e-05 -0.0001 -0.0026 -0.00046 -9.7e-05 -0.0013 -0.00066 -0.0001 -0.0035 -0.00035 -0.00046 -0.00044 -0.0002 -0.00039 -0.0014 -0.0072 -0.0033 -0.00083 -0.00069 -0.00097 -0.00019 -0.00074 -3.2e-06 0.0033 -0.0011 -0.0015 -4.5e-06 -0.0021 -0.0001
Interaction_MYYear_15Month_1 0.0098 0.0031 0.034 -0.00046 -0.00066 0.0043 -0.01 -0.0023 -0.00034 0.00037 -0.00092 -0.00081 0.026 -0.00035 0.026 0.014 0.0021 0.017 0.043 -0.00025 -0.011 -0.0001 0.044 -0.00042 -0.0006 0.0041 -0.00014 -0.0002 -0.0005 5.6e-05 9e-05 0.0013 -0.0003 -0.0006 0.00021 -0.00083 0.0038 -0.00035 0.0017 0.0055 0.0039 0.0011 0.0024 0.00088 0.00059 -8.2e-05 0.00072 -0.00087 -0.0045 -0.00057 -4.8e-05 -0.00028 8.8e-05 0.0019 nan -0.00042 0.00054 0.00035 -0.00067 -0.0025 0.00028 -4.2e-05 -3.1e-05 -4e-05 1 -3.9e-05 -0.00031 -0.00013 -6.7e-05 -0.0003 -0.0002 -0.0002 -0.00027 -0.00015 -0.0002 -0.00019 -0.00021 -0.00042 -0.00023 -0.00025 -0.00038 -0.00027 -0.00038 -4.5e-05 -6.2e-05 -0.0016 -0.00029 -6e-05 -0.00081 -0.00041 -6.5e-05 -0.0022 -0.00021 -0.00029 -0.00027 -0.00013 -0.00024 -0.00086 -0.0045 -0.002 -0.00051 -0.00042 -0.0006 -0.00012 -0.00033 -2e-06 0.001 -0.00068 -0.00067 -2.8e-06 -0.00055 0.00027
Interaction_MYYear_15Month_3 0.046 0.0022 0.033 -0.00039 -0.001 0.0042 -0.044 -0.0028 0.00082 -0.00055 -0.0017 -0.00093 0.032 0.0057 0.068 0.044 0.00042 0.029 0.067 0.01 -0.004 -0.00016 0.075 -0.0012 0.00017 0.0086 -0.0018 -0.00032 0.00028 0.0022 -1.2e-05 0.0035 0.00022 -0.00098 -0.0003 -0.00053 0.0059 0.0051 0.0024 0.011 0.0055 -0.0014 0.0003 0.0016 -0.00026 -0.00022 0.00016 0.00083 -0.0071 -0.0011 -7.7e-05 -0.00042 0.0012 -0.003 nan -0.00017 -0.0013 -0.0014 -0.0017 -0.0044 0.0012 -6.7e-05 -5e-05 -6.3e-05 -3.9e-05 1 -0.00049 -0.00021 -0.00011 -0.00047 -0.00032 -0.00032 -0.00042 -0.00024 -0.00031 -0.0003 -0.00033 -0.00066 -0.00036 -0.0004 -0.00059 -0.00042 -0.0006 -7.1e-05 -9.9e-05 -0.0025 -0.00045 -9.5e-05 -0.0013 -0.00065 -0.0001 -0.0035 -0.00034 -0.00045 -0.00043 -0.0002 -0.00039 -0.0014 -0.0071 -0.0032 -0.00081 -0.00067 -0.00095 -0.00018 -0.0014 -3.2e-06 0.0015 -0.0011 -0.002 -4.4e-06 -0.0028 -0.0021
Interaction_MYYear_15Month_7 -0.0041 0.015 0.13 -0.0074 -0.0083 -0.0022 0.0044 0.03 -0.01 -0.006 -0.023 -0.015 -0.0096 0.0023 -0.0087 0.12 -0.055 0.13 0.04 0.095 -0.036 -0.0013 0.072 0.0028 -0.0034 -0.0021 -0.014 -0.0025 -0.00044 -0.0032 -0.00043 0.0044 0.002 0.0082 -0.00078 -0.0069 0.023 0.0022 0.11 0.14 0.026 0.051 0.023 -0.023 -0.031 -0.029 -0.076 0.077 0.06 0.12 -8.2e-05 0.0027 -0.0038 -0.044 nan 0.005 -0.016 -0.011 -0.014 0.00077 0.0047 -0.00053 -0.0004 -0.0005 -0.00031 -0.00049 1 -0.0017 -0.00085 -0.0037 -0.0026 -0.0025 -0.0033 -0.0019 -0.0025 -0.0024 -0.0026 -0.0053 -0.0029 -0.0032 -0.0047 -0.0033 -0.0047 -0.00057 -0.00079 -0.02 -0.0036 -0.00076 -0.01 -0.0052 -0.00081 -0.028 -0.0027 -0.0036 -0.0035 -0.0016 -0.0031 -0.011 -0.056 -0.026 -0.0065 -0.0053 -0.0075 -0.0015 0.012 -2.5e-05 0.021 -0.0074 -0.015 -3.4e-05 -0.022 -0.014
Interaction_MYYear_15Month_8 -0.0027 0.0076 0.057 -0.0033 -0.0036 -0.00086 0.0028 0.014 -0.0038 -0.003 -0.0055 -0.0029 -0.0044 0.005 -0.0039 0.053 -0.013 0.057 0.15 0.012 -0.015 -0.00056 0.064 0.0022 -0.00043 -0.0013 0.00016 -0.0011 0.00064 -0.002 0.00017 0.0017 0.00076 0.003 -0.00074 -0.0019 0.12 0.0049 0.053 0.059 0.12 0.0045 -0.003 0.00026 -0.0087 -0.01 -0.032 0.016 0.031 0.05 -0.00026 0.00036 -0.0014 -0.0097 nan -0.00055 -0.0004 -0.002 0.0018 0.00046 0.0015 -0.00023 -0.00017 -0.00022 -0.00013 -0.00021 -0.0017 1 -0.00036 -0.0016 -0.0011 -0.0011 -0.0014 -0.00083 -0.0011 -0.001 -0.0011 -0.0023 -0.0012 -0.0014 -0.002 -0.0014 -0.002 -0.00024 -0.00034 -0.0086 -0.0015 -0.00032 -0.0044 -0.0022 -0.00035 -0.012 -0.0012 -0.0015 -0.0015 -0.00068 -0.0013 -0.0047 -0.024 -0.011 -0.0028 -0.0023 -0.0032 -0.00063 0.0064 -1.1e-05 0.0042 -0.002 -0.0085 -1.5e-05 -0.0093 -0.0021
Interaction_MYYear_15Month_9 0.002 0.0047 0.03 -0.0018 -0.0018 0.00048 -0.0019 0.0034 -0.00095 -0.0015 0.00068 6.5e-05 -0.00036 0.0036 -0.0002 0.027 0.0034 0.03 0.1 0.00026 -0.0083 -0.00028 0.037 -0.00023 0.00034 0.00016 0.0041 -0.00054 0.0026 -0.00047 -0.0012 -0.0013 0.0013 0.0014 -0.0015 -0.00039 0.071 0.0036 0.026 0.029 0.065 0.003 0.0045 0.0085 -0.0035 -0.0044 -0.014 0.00089 0.012 0.022 -0.00013 0.0016 -0.00033 0.0014 nan -0.00085 0.0024 -0.0018 0.0042 -0.00022 0.00035 -0.00011 -8.5e-05 -0.00011 -6.7e-05 -0.00011 -0.00085 -0.00036 1 -0.00081 -0.00055 -0.00054 -0.00072 -0.00042 -0.00054 -0.00051 -0.00056 -0.0011 -0.00062 -0.00068 -0.001 -0.00072 -0.001 -0.00012 -0.00017 -0.0043 -0.00077 -0.00016 -0.0022 -0.0011 -0.00018 -0.0059 -0.00058 -0.00077 -0.00074 -0.00034 -0.00066 -0.0023 -0.012 -0.0055 -0.0014 -0.0012 -0.0016 -0.00032 0.0012 -5.4e-06 -0.00011 -0.001 -0.0064 -7.6e-06 -0.0049 0.0022
Interaction_MYYear_15Month_10 -0.0019 0.0076 0.069 -0.0064 -0.0079 -0.0039 0.002 -0.0031 -0.0032 -0.0016 -0.013 -0.0033 -0.009 0.0095 -0.0082 0.087 -0.026 0.094 0.067 0.016 -0.013 -0.0012 0.13 0.00043 -0.0033 -0.0011 0.0024 -0.0024 0.0028 5.9e-05 0.0024 0.0087 0.023 -0.012 -0.0019 0.0021 0.045 0.0095 0.03 0.096 -0.034 -0.0057 -0.019 0.0017 -0.048 -0.0085 -0.064 0.069 -0.0097 -0.0094 -0.00058 -0.00088 0.0013 -0.029 nan 0.0064 -0.0069 -0.0086 -0.0028 -0.006 -0.0013 -0.00051 -0.00038 -0.00048 -0.0003 -0.00047 -0.0037 -0.0016 -0.00081 1 -0.0024 -0.0024 -0.0032 -0.0019 -0.0024 -0.0023 -0.0025 -0.005 -0.0027 -0.003 -0.0045 -0.0032 -0.0045 -0.00054 -0.00075 -0.019 -0.0034 -0.00072 -0.0097 -0.0049 -0.00078 -0.026 -0.0026 -0.0034 -0.0033 -0.0015 -0.0029 -0.01 -0.054 -0.024 -0.0062 -0.0051 -0.0072 -0.0014 -0.00048 -2.4e-05 0.02 -0.0061 -0.0099 -3.3e-05 -0.016 -0.0092
Interaction_MYYear_15Month_11 -0.0041 0.017 0.048 -0.0048 -0.0054 -0.0013 0.0042 0.0095 -0.0047 -0.0037 -0.0087 -0.0039 -0.0061 0.011 -0.0055 0.06 -0.0047 0.065 0.12 0.0011 -0.023 -0.00085 0.077 0.00016 0.00049 -0.00044 0.0052 -0.0016 0.0024 -0.0033 -0.0024 -0.0017 0.007 -0.007 -0.0046 0.00097 0.095 0.011 0.02 0.068 0.099 0.00026 -0.0044 0.013 -0.025 -0.014 -0.044 0.028 0.0071 -0.0065 3.9e-06 0.0054 -0.00058 -0.0063 nan -0.0022 0.0027 -0.0019 0.0078 -0.0039 0.0019 -0.00035 -0.00026 -0.00033 -0.0002 -0.00032 -0.0026 -0.0011 -0.00055 -0.0024 1 -0.0017 -0.0022 -0.0013 -0.0016 -0.0015 -0.0017 -0.0035 -0.0019 -0.0021 -0.0031 -0.0022 -0.0031 -0.00037 -0.00051 -0.013 -0.0024 -0.00049 -0.0067 -0.0034 -0.00053 -0.018 -0.0018 -0.0024 -0.0023 -0.001 -0.002 -0.0071 -0.037 -0.017 -0.0042 -0.0035 -0.0049 -0.00096 0.0048 -1.6e-05 0.0037 -0.0036 -0.017 -2.3e-05 -0.015 -0.0002
Interaction_MYYear_16Month_1 0.0045 0.011 0.053 -0.004 -0.0054 -0.00058 -0.0041 -0.0044 -4.4e-05 0.0001 -0.0092 -0.0042 0.00063 0.01 0.0012 0.061 -0.01 0.067 0.078 0.0076 -0.011 -0.00084 0.057 -0.0015 -0.0015 0.0023 0.0042 -0.0016 0.0071 0.002 0.0021 0.0084 0.015 -0.0072 -0.00058 0.0026 0.047 0.01 0.022 0.065 0.047 -0.003 -0.008 0.0042 -0.0032 -0.0041 -0.034 0.027 -0.0065 -0.0049 -0.00039 0.0048 0.0041 -0.016 nan 0.0043 -0.00085 -0.0038 0.0018 -0.0076 0.0012 -0.00034 -0.00025 -0.00032 -0.0002 -0.00032 -0.0025 -0.0011 -0.00054 -0.0024 -0.0017 1 -0.0022 -0.0013 -0.0016 -0.0015 -0.0017 -0.0034 -0.0018 -0.002 -0.003 -0.0021 -0.0031 -0.00036 -0.00051 -0.013 -0.0023 -0.00049 -0.0066 -0.0033 -0.00052 -0.018 -0.0017 -0.0023 -0.0022 -0.001 -0.002 -0.007 -0.036 -0.017 -0.0042 -0.0034 -0.0049 -0.00095 -0.0031 -1.6e-05 0.013 -0.0038 -0.0073 -2.3e-05 -0.0094 -0.0043
Interaction_MYYear_16Month_2 0.0064 0.02 0.065 -0.0052 -0.0071 -0.00028 -0.0059 0.0021 0.00041 -0.002 -0.0042 -0.0034 -0.0058 0.0052 -0.005 0.08 0.0042 0.087 0.076 -0.0041 -0.023 -0.0011 0.071 -0.0016 -0.00046 -5.3e-05 0.0084 -0.0021 0.0025 -0.0022 0.00073 -0.00086 0.013 -0.0094 -0.0023 0.0015 -0.0046 0.0052 0.027 0.087 -0.005 0.002 0.0031 0.024 -0.006 -0.0067 -0.052 0.02 0.013 -0.0078 -0.00052 -0.0013 -0.00073 0.00059 nan 0.001 -5e-06 -0.0065 0.0091 -0.0085 0.00016 -0.00045 -0.00034 -0.00043 -0.00027 -0.00042 -0.0033 -0.0014 -0.00072 -0.0032 -0.0022 -0.0022 1 -0.0017 -0.0021 -0.002 -0.0022 -0.0045 -0.0024 -0.0027 -0.004 -0.0028 -0.004 -0.00048 -0.00067 -0.017 -0.0031 -0.00064 -0.0087 -0.0044 -0.00069 -0.024 -0.0023 -0.0031 -0.0029 -0.0014 -0.0026 -0.0093 -0.048 -0.022 -0.0055 -0.0046 -0.0064 -0.0013 -0.0019 -2.1e-05 0.0022 -0.0028 -0.012 -3e-05 -0.014 -0.0016
Interaction_MYYear_16Month_3 0.016 0.027 0.056 -0.0032 -0.0041 0.0031 -0.015 0.0027 4.5e-05 -0.00088 0.0018 0.0013 0.022 0.0068 0.023 0.053 0.0092 0.059 0.1 -0.006 -0.026 -0.00064 0.056 -0.003 -0.0011 0.0017 0.0068 -0.00099 0.002 -0.00084 0.0017 0.00093 0.0021 -0.0041 0.0038 0.0022 0.033 0.0069 0.013 0.05 0.034 -0.00077 0.0042 0.016 -0.0046 -0.0053 -0.022 -0.0031 0.0052 -0.0058 0.00023 0.0026 -0.0012 0.0033 nan -0.0021 0.0066 -0.0035 0.011 -0.007 0.0038 -0.00026 -0.0002 -0.00025 -0.00015 -0.00024 -0.0019 -0.00083 -0.00042 -0.0019 -0.0013 -0.0013 -0.0017 1 -0.0012 -0.0012 -0.0013 -0.0026 -0.0014 -0.0016 -0.0023 -0.0017 -0.0023 -0.00028 -0.00039 -0.0099 -0.0018 -0.00037 -0.0051 -0.0026 -0.0004 -0.014 -0.0013 -0.0018 -0.0017 -0.00079 -0.0015 -0.0054 -0.028 -0.013 -0.0032 -0.0026 -0.0037 -0.00073 -0.0028 -1.2e-05 -0.0024 -0.0021 -0.0096 -1.8e-05 -0.009 0.00083
Interaction_MYYear_16Month_4 0.11 0.022 0.085 -0.0039 -0.0053 0.0061 -0.1 -0.0032 0.0046 -0.0014 -0.0028 -9.5e-06 0.029 0.016 0.029 0.084 0.0055 0.091 0.04 0.018 -0.014 -0.00083 0.095 -0.0061 0.0017 0.022 0.0031 -0.0016 0.0033 0.0052 0.0049 0.0078 0.0089 -0.0036 -0.0023 0.0015 0.026 0.016 0.021 0.066 0.028 -0.0081 -0.0018 0.013 -0.0018 -0.0012 -0.019 -0.0039 -0.009 -0.0044 -0.00039 0.0057 0.0021 -0.01 nan -0.0021 -0.0011 -0.004 0.0046 -0.013 0.0077 -0.00034 -0.00025 -0.00032 -0.0002 -0.00031 -0.0025 -0.0011 -0.00054 -0.0024 -0.0016 -0.0016 -0.0021 -0.0012 1 -0.0015 -0.0016 -0.0034 -0.0018 -0.002 -0.003 -0.0021 -0.003 -0.00036 -0.0005 -0.013 -0.0023 -0.00048 -0.0065 -0.0033 -0.00052 -0.018 -0.0017 -0.0023 -0.0022 -0.001 -0.002 -0.0069 -0.036 -0.016 -0.0041 -0.0034 -0.0048 -0.00093 -0.0062 -1.6e-05 0.0014 -0.0025 -0.0089 -2.3e-05 -0.012 -0.0037
Interaction_MYYear_16Month_5 -0.0012 0.028 0.044 -0.0035 -0.005 0.00069 0.001 0.0031 0.00028 -0.00097 -0.0055 -0.0026 -0.0058 0.0075 -0.0054 0.055 0.0004 0.059 0.031 -0.003 -0.019 -0.00078 0.039 -0.0022 0.00018 -0.00093 -0.00043 -0.0015 0.0015 -0.00039 0.0016 0.0027 0.0065 -0.0041 0.00015 -0.0054 0.02 0.0075 0.017 0.062 0.022 -0.0059 -0.0043 0.0098 -0.0087 -0.0078 -0.028 0.001 0.0044 -0.0068 6.8e-05 0.0033 -0.00093 -0.0019 nan -0.0015 0.0017 -0.0026 0.0003 -0.0084 0.0037 -0.00032 -0.00024 -0.0003 -0.00019 -0.0003 -0.0024 -0.001 -0.00051 -0.0023 -0.0015 -0.0015 -0.002 -0.0012 -0.0015 1 -0.0016 -0.0032 -0.0017 -0.0019 -0.0029 -0.002 -0.0029 -0.00034 -0.00047 -0.012 -0.0022 -0.00046 -0.0062 -0.0031 -0.00049 -0.017 -0.0016 -0.0022 -0.0021 -0.00096 -0.0019 -0.0066 -0.034 -0.015 -0.0039 -0.0032 -0.0045 -0.00089 -0.0039 -1.5e-05 0.00084 -0.0024 -0.0062 -2.1e-05 -0.011 -0.0011
Interaction_MYYear_16Month_6 -0.0038 0.022 0.11 -0.0039 -0.0055 -0.00012 0.0032 0.0041 0.00019 -0.0016 -0.011 -0.0037 0.00016 0.0064 0.00073 0.062 -0.017 0.068 0.066 0.01 -0.014 -0.00086 0.095 -0.00063 -0.00045 -0.0021 0.0032 -0.0017 0.0014 0.00091 0.0062 0.0088 0.014 -0.00085 0.0002 0.0039 0.061 0.0065 0.025 0.069 0.062 -0.0047 -0.014 -0.0034 -0.016 -0.015 -0.031 0.014 0.00087 -0.0046 0.0004 0.0011 0.00047 -0.016 nan 0.0011 -0.0016 -0.0041 -0.00087 -0.011 0.0017 -0.00035 -0.00026 -0.00033 -0.00021 -0.00033 -0.0026 -0.0011 -0.00056 -0.0025 -0.0017 -0.0017 -0.0022 -0.0013 -0.0016 -0.0016 1 -0.0035 -0.0019 -0.0021 -0.0031 -0.0022 -0.0031 -0.00037 -0.00052 -0.013 -0.0024 -0.0005 -0.0067 -0.0034 -0.00054 -0.018 -0.0018 -0.0024 -0.0023 -0.001 -0.002 -0.0072 -0.037 -0.017 -0.0043 -0.0035 -0.005 -0.00097 -0.0042 -1.7e-05 0.011 -0.0036 -0.0058 -2.3e-05 -0.013 -0.006
Interaction_MYYear_16Month_7 -0.0086 0.013 0.056 0.0014 0.00028 -0.0032 0.0088 -0.0094 -0.0028 -0.0012 -0.017 -0.0098 -0.014 -0.0039 -0.013 0.077 -0.0081 0.083 -0.0069 0.0043 -0.018 -0.0017 -0.037 0.0014 -0.0032 -0.0031 -0.00081 -0.002 0.0034 -0.0041 0.0018 -0.00025 -0.0036 -0.014 5.3e-06 -0.0025 -0.0034 -0.0039 -0.036 0.086 -0.0038 0.0035 -0.0052 0.019 -0.011 -0.014 -0.075 0.06 0.0032 -0.0093 -3.6e-05 -0.003 -0.0033 -0.017 nan 0.0047 -0.0057 -0.0081 0.00015 -0.019 -0.0047 -0.00072 -0.00053 -0.00068 -0.00042 -0.00066 -0.0053 -0.0023 -0.0011 -0.005 -0.0035 -0.0034 -0.0045 -0.0026 -0.0034 -0.0032 -0.0035 1 -0.0039 -0.0043 -0.0064 -0.0045 -0.0064 -0.00076 -0.0011 -0.027 -0.0048 -0.001 -0.014 -0.007 -0.0011 -0.037 -0.0036 -0.0048 -0.0047 -0.0021 -0.0041 -0.015 -0.076 -0.035 -0.0087 -0.0072 -0.01 -0.002 -0.001 -3.4e-05 0.02 -0.0061 -0.0082 -4.7e-05 -0.018 -0.0073
Interaction_MYYear_16Month_8 -0.004 0.036 0.12 -0.0033 -0.0039 0.0042 0.0035 -0.00078 0.0023 -0.0018 -6.1e-05 0.00085 0.01 0.011 0.011 0.064 0.016 0.071 0.13 -0.015 -0.029 -0.00095 0.039 -0.0049 0.0019 -0.0025 0.0057 -0.0018 0.0011 -0.00084 0.0052 0.0035 0.0073 0.0015 0.00044 -0.0032 0.05 0.011 0.014 0.068 0.053 -0.0073 0.0056 0.022 -0.0027 -0.0032 -0.015 -0.0065 -0.0027 -0.0038 0.00028 0.0067 -0.00018 0.0093 nan -0.0025 0.0075 -0.0031 0.0081 -0.012 0.0066 -0.00039 -0.00029 -0.00037 -0.00023 -0.00036 -0.0029 -0.0012 -0.00062 -0.0027 -0.0019 -0.0018 -0.0024 -0.0014 -0.0018 -0.0017 -0.0019 -0.0039 1 -0.0023 -0.0035 -0.0024 -0.0035 -0.00041 -0.00057 -0.015 -0.0026 -0.00055 -0.0074 -0.0038 -0.00059 -0.02 -0.002 -0.0026 -0.0025 -0.0012 -0.0022 -0.0079 -0.041 -0.019 -0.0047 -0.0039 -0.0055 -0.0011 -0.0039 -1.8e-05 -0.005 -0.0013 -0.011 -2.6e-05 -0.014 0.0019
Interaction_MYYear_16Month_9 0.014 0.037 0.11 -0.0012 -0.0017 0.004 -0.013 0.0037 -0.00016 -0.0018 0.0022 0.0057 0.014 0.0054 0.015 0.066 0.014 0.074 0.16 -0.01 -0.03 -0.001 0.043 -0.0053 0.00081 0.0021 -0.0026 -0.0017 -0.0023 -0.0038 0.0057 -0.0012 -0.00069 -0.0026 -0.0024 -0.009 0.1 0.0055 0.00061 0.067 0.1 -0.0025 0.006 0.026 -0.0056 -0.0044 -0.019 -0.0051 0.0068 -0.0042 -0.00016 0.01 -0.0073 0.013 nan -0.0025 0.0048 -0.0032 0.0029 -0.012 0.0032 -0.00043 -0.00032 -0.00041 -0.00025 -0.0004 -0.0032 -0.0014 -0.00068 -0.003 -0.0021 -0.002 -0.0027 -0.0016 -0.002 -0.0019 -0.0021 -0.0043 -0.0023 1 -0.0038 -0.0027 -0.0038 -0.00046 -0.00063 -0.016 -0.0029 -0.00061 -0.0082 -0.0042 -0.00066 -0.022 -0.0022 -0.0029 -0.0028 -0.0013 -0.0025 -0.0088 -0.045 -0.021 -0.0052 -0.0043 -0.0061 -0.0012 -0.0044 -2e-05 -0.012 -0.00032 -0.009 -2.9e-05 -0.016 -0.0013
Interaction_MYYear_16Month_10 0.0069 0.052 0.16 -0.0028 -0.0037 0.0052 -0.0063 -0.005 0.0042 -0.0032 -0.0029 -8.2e-05 -0.0091 0.013 -0.0081 0.097 0.026 0.1 0.19 -0.023 -0.036 -0.0016 0.16 -0.0088 0.0025 -0.00013 0.011 -0.003 0.0019 -0.0035 0.0004 -0.003 0.005 0.00093 0.002 -0.00047 0.16 0.013 0.088 0.11 0.17 -0.0073 0.01 0.04 -0.0042 -0.0059 -0.036 -0.0086 0.0066 -0.012 -7.6e-05 0.00013 -0.00068 0.015 nan -0.0036 0.01 -0.0036 0.022 -0.019 0.013 -0.00064 -0.00048 -0.00061 -0.00038 -0.00059 -0.0047 -0.002 -0.001 -0.0045 -0.0031 -0.003 -0.004 -0.0023 -0.003 -0.0029 -0.0031 -0.0064 -0.0035 -0.0038 1 -0.004 -0.0057 -0.00068 -0.00095 -0.024 -0.0043 -0.00091 -0.012 -0.0062 -0.00098 -0.033 -0.0032 -0.0043 -0.0042 -0.0019 -0.0037 -0.013 -0.068 -0.031 -0.0078 -0.0064 -0.0091 -0.0018 -0.0082 -3e-05 -0.01 0.00058 -0.023 -4.3e-05 -0.021 0.0076
Interaction_MYYear_16Month_11 -0.0052 0.032 0.063 2.7e-05 0.00095 0.0029 0.0052 -0.0088 0.0024 -0.0012 -0.0066 -0.0029 -0.0084 0.0042 -0.0087 0.053 0.0032 0.057 0.087 -0.004 -0.022 -0.0011 0.0021 -0.0019 0.00022 -0.0018 0.0048 0.00087 0.0024 -0.0002 0.0029 0.0026 -0.0016 -0.0032 0.0025 0.00032 0.066 0.0043 -0.015 0.06 0.065 -0.0021 0.00075 0.01 -0.002 -0.0041 -0.026 0.0076 -0.00025 -0.00073 -0.00021 0.0025 -7.2e-05 -0.0021 nan -0.00057 0.0028 -0.0023 0.0073 -0.013 0.0024 -0.00045 -0.00034 -0.00043 -0.00027 -0.00042 -0.0033 -0.0014 -0.00072 -0.0032 -0.0022 -0.0021 -0.0028 -0.0017 -0.0021 -0.002 -0.0022 -0.0045 -0.0024 -0.0027 -0.004 1 -0.004 -0.00048 -0.00067 -0.017 -0.0031 -0.00064 -0.0087 -0.0044 -0.00069 -0.023 -0.0023 -0.0031 -0.0029 -0.0013 -0.0026 -0.0092 -0.048 -0.022 -0.0055 -0.0045 -0.0064 -0.0012 -0.0025 -2.1e-05 0.00099 -0.0011 -0.0085 -3e-05 -0.011 -0.0009
Interaction_MYYear_16Month_12 -0.0073 0.048 0.06 0.0024 0.0043 0.0072 0.0068 -0.0099 0.006 -0.0023 -0.0045 -0.001 -0.012 0.0049 -0.013 0.069 0.018 0.074 0.14 -0.018 -0.033 -0.0016 -0.034 -0.0056 0.0031 -0.0033 -0.0024 -0.00011 0.00079 0.0014 0.0093 0.0025 -0.01 -0.0023 0.0029 -0.0088 0.1 0.005 -0.032 0.079 0.1 -0.0056 0.0065 0.023 0.00041 -0.0031 -0.027 -0.011 -0.003 -0.013 0.00014 0.004 -0.00044 0.011 nan -0.0028 0.0019 -0.0033 0.0032 -0.02 0.011 -0.00064 -0.00048 -0.00061 -0.00038 -0.0006 -0.0047 -0.002 -0.001 -0.0045 -0.0031 -0.0031 -0.004 -0.0023 -0.003 -0.0029 -0.0031 -0.0064 -0.0035 -0.0038 -0.0057 -0.004 1 -0.00068 -0.00095 -0.024 -0.0043 -0.00091 -0.012 -0.0062 -0.00098 -0.033 -0.0033 -0.0043 -0.0042 -0.0019 -0.0037 -0.013 -0.068 -0.031 -0.0078 -0.0064 -0.0091 -0.0018 -0.0065 -3e-05 -0.013 0.0028 -0.0075 -4.3e-05 -0.016 -0.0012
Interaction_MYYear_17Month_1 -0.00093 0.0011 0.029 -0.00022 2.1e-05 -0.00088 0.00099 0.0017 -0.0004 0.0021 -0.0013 0.0013 -0.0016 0.0048 -0.0015 0.0082 0.00067 0.0089 0.076 -0.0013 -0.00082 -0.00019 0.077 0.00077 -0.00027 0.0033 0.013 -0.00036 0.013 0.0041 -0.0017 -0.0015 0.0041 -0.0016 0.0095 0.0098 0.043 0.0048 -0.0038 0.0095 0.0068 -0.0024 -0.0012 0.005 -0.00068 -0.00041 0.0014 0.0051 0.0042 0.0017 -8.8e-05 -0.00063 0.0047 -0.0042 nan -0.00055 0.0069 -0.00068 0.017 -0.00097 0.00069 -7.7e-05 -5.7e-05 -7.3e-05 -4.5e-05 -7.1e-05 -0.00057 -0.00024 -0.00012 -0.00054 -0.00037 -0.00036 -0.00048 -0.00028 -0.00036 -0.00034 -0.00037 -0.00076 -0.00041 -0.00046 -0.00068 -0.00048 -0.00068 1 -0.00011 -0.0029 -0.00052 -0.00011 -0.0015 -0.00074 -0.00012 -0.004 -0.00039 -0.00052 -0.0005 -0.00023 -0.00044 -0.0016 -0.0081 -0.0037 -0.00093 -0.00077 -0.0011 -0.00021 -0.00036 -3.6e-06 0.0032 -0.00096 -0.0054 -5.2e-06 -0.00019 0.0063
Interaction_MYYear_17Month_2 0.037 0.013 0.03 0.00032 -0.0017 0.0024 -0.036 -0.0039 0.0001 -2.9e-05 -0.0028 -0.0023 0.062 0.0026 0.062 0.036 0.00028 0.043 0.037 0.0058 -0.015 -0.00026 0.054 -0.00075 -0.0025 0.011 -0.0015 -0.0005 0.00019 0.001 5.3e-05 0.0037 -0.00099 -0.00081 -0.00075 -0.0012 0.011 0.0023 0.0032 0.018 0.011 2e-05 0.0017 0.0014 -0.00071 -0.00093 -0.00026 0.00029 -0.011 -0.0022 -0.00012 -0.00063 0.00045 0.00086 nan -0.00029 -0.0011 -0.001 -0.0017 -0.0075 0.0016 -0.00011 -7.9e-05 -0.0001 -6.2e-05 -9.9e-05 -0.00079 -0.00034 -0.00017 -0.00075 -0.00051 -0.00051 -0.00067 -0.00039 -0.0005 -0.00047 -0.00052 -0.0011 -0.00057 -0.00063 -0.00095 -0.00067 -0.00095 -0.00011 1 -0.004 -0.00072 -0.00015 -0.002 -0.001 -0.00016 -0.0055 -0.00054 -0.00072 -0.00069 -0.00032 -0.00061 -0.0022 -0.011 -0.0051 -0.0013 -0.0011 -0.0015 -0.00029 -0.00011 -5e-06 0.0015 -0.0013 -0.0016 -6.9e-06 -0.0031 -0.0024
Interaction_MYYear_17Month_3 -0.031 0.059 0.15 -0.0062 -0.012 -0.00068 0.03 -0.042 0.003 0.0017 -0.0033 -0.023 -0.056 -0.01 -0.052 0.13 0.068 0.14 0.021 -0.077 -0.051 -0.0066 0.17 -0.025 0.0046 -0.0085 0.0059 -0.012 0.01 -0.015 0.0028 -0.036 -0.018 -0.021 0.021 -0.024 0.25 -0.01 0.49 0.16 0.23 -0.011 0.026 0.15 -0.0098 -0.021 0.14 -0.0051 0.068 -0.048 -0.0012 0.0088 -0.011 0.07 nan -0.0078 0.014 0.0025 0.028 -0.027 -0.0039 -0.0027 -0.002 -0.0026 -0.0016 -0.0025 -0.02 -0.0086 -0.0043 -0.019 -0.013 -0.013 -0.017 -0.0099 -0.013 -0.012 -0.013 -0.027 -0.015 -0.016 -0.024 -0.017 -0.024 -0.0029 -0.004 1 -0.018 -0.0039 -0.052 -0.026 -0.0041 -0.14 -0.014 -0.018 -0.018 -0.0081 -0.016 -0.055 -0.29 -0.13 -0.033 -0.027 -0.038 -0.0075 -0.022 -0.00013 -0.028 0.012 -0.026 -0.00018 -0.031 0.028
Interaction_MYYear_17Month_4 0.0036 0.041 0.13 -0.0017 -0.0013 0.0037 -0.0034 -0.0078 0.002 -0.0014 -0.0023 0.00035 0.0025 0.0071 0.0022 0.066 0.015 0.072 0.14 -0.013 -0.028 -0.0012 0.096 -0.0043 0.0014 -0.00015 0.0015 0.00062 0.0014 -0.0011 0.0028 0.0012 -0.0033 0.0029 0.0014 -0.0057 0.13 0.0071 0.018 0.07 0.14 -0.0038 0.0063 0.02 -0.00098 -0.003 0.021 -0.0092 -0.0046 -0.0056 -0.00027 0.0042 -0.0021 0.0094 nan -0.0017 0.0059 -0.002 0.0072 -0.017 0.0059 -0.00049 -0.00036 -0.00046 -0.00029 -0.00045 -0.0036 -0.0015 -0.00077 -0.0034 -0.0024 -0.0023 -0.0031 -0.0018 -0.0023 -0.0022 -0.0024 -0.0048 -0.0026 -0.0029 -0.0043 -0.0031 -0.0043 -0.00052 -0.00072 -0.018 1 -0.00069 -0.0093 -0.0047 -0.00075 -0.025 -0.0025 -0.0033 -0.0032 -0.0015 -0.0028 -0.01 -0.052 -0.023 -0.0059 -0.0049 -0.0069 -0.0013 -0.0035 -2.3e-05 -0.01 0.0013 -0.013 -3.3e-05 -0.015 0.0014
Interaction_MYYear_17Month_5 0.059 0.0092 0.0066 -0.00023 -0.0016 0.0055 -0.056 -0.0035 0.00083 0.00041 -0.0022 -0.0017 0.052 0.0037 0.052 0.037 -0.00093 0.043 0.074 0.01 -0.0077 -0.00025 0.077 -0.0021 -0.00025 0.015 -0.002 -0.00048 -0.00064 0.0015 0.001 0.0046 -0.0014 -0.00081 -0.0012 -0.0013 0.01 0.004 0.0036 0.017 0.01 -0.0016 -0.00024 0.0016 0.00032 0.0002 0.00026 -0.0011 -0.011 -0.0021 -0.00012 -3e-05 0.001 -0.0024 nan 9.8e-05 -0.0011 -0.0014 -0.002 -0.0071 0.0017 -0.0001 -7.6e-05 -9.7e-05 -6e-05 -9.5e-05 -0.00076 -0.00032 -0.00016 -0.00072 -0.00049 -0.00049 -0.00064 -0.00037 -0.00048 -0.00046 -0.0005 -0.001 -0.00055 -0.00061 -0.00091 -0.00064 -0.00091 -0.00011 -0.00015 -0.0039 -0.00069 1 -0.002 -0.00099 -0.00016 -0.0053 -0.00052 -0.00069 -0.00066 -0.00031 -0.00059 -0.0021 -0.011 -0.0049 -0.0012 -0.001 -0.0015 -0.00028 -0.0019 -4.8e-06 0.00033 -0.001 -0.0019 -6.8e-06 -0.0033 -0.0026
Interaction_MYYear_17Month_6 -0.016 0.028 0.095 -0.015 -0.02 0.00089 0.017 0.014 -0.0091 -0.011 -0.032 -0.016 -0.024 0.038 -0.023 0.3 -0.059 0.33 0.11 0.12 -0.046 -0.0034 0.2 0.0077 -0.0047 -0.008 0.00011 -0.0029 0.0049 -0.0096 -0.0027 0.0053 0.015 0.014 -0.0016 -0.008 0.082 0.038 0.2 0.35 0.092 0.07 0.048 0.0024 -0.037 -0.041 -0.16 0.11 0.13 0.25 -0.00044 0.01 -0.01 -0.051 nan 0.01 -0.003 -0.018 0.012 -0.014 0.0036 -0.0014 -0.001 -0.0013 -0.00081 -0.0013 -0.01 -0.0044 -0.0022 -0.0097 -0.0067 -0.0066 -0.0087 -0.0051 -0.0065 -0.0062 -0.0067 -0.014 -0.0074 -0.0082 -0.012 -0.0087 -0.012 -0.0015 -0.002 -0.052 -0.0093 -0.002 1 -0.013 -0.0021 -0.072 -0.007 -0.0093 -0.009 -0.0041 -0.008 -0.028 -0.15 -0.067 -0.017 -0.014 -0.02 -0.0038 0.029 -6.5e-05 0.014 -0.01 -0.051 -9e-05 -0.052 -0.0086
Interaction_MYYear_17Month_7 -0.0017 0.055 0.18 -0.0033 -0.003 0.0043 0.0021 -0.0075 0.0021 -0.0027 -0.0017 0.0023 -0.0006 0.013 -0.0021 0.09 0.029 0.099 0.17 -0.026 -0.039 -0.0017 0.12 -0.0069 0.0037 -0.0028 0.0098 0.0039 0.0019 -0.0047 0.0013 -0.003 -0.00022 0.0035 0.0039 -0.0043 0.13 0.013 0.037 0.1 0.14 -0.0078 0.011 0.035 -0.0042 -0.0058 0.041 -0.016 -0.0015 -0.016 2.1e-06 0.0073 -0.0047 0.02 nan -0.0028 0.013 -0.0015 0.021 -0.023 0.0073 -0.0007 -0.00052 -0.00066 -0.00041 -0.00065 -0.0052 -0.0022 -0.0011 -0.0049 -0.0034 -0.0033 -0.0044 -0.0026 -0.0033 -0.0031 -0.0034 -0.007 -0.0038 -0.0042 -0.0062 -0.0044 -0.0062 -0.00074 -0.001 -0.026 -0.0047 -0.00099 -0.013 1 -0.0011 -0.036 -0.0035 -0.0047 -0.0045 -0.0021 -0.004 -0.014 -0.074 -0.034 -0.0085 -0.007 -0.0099 -0.0019 -0.0066 -3.3e-05 -0.015 0.0012 -0.021 -4.7e-05 -0.023 0.0074
Interaction_MYYear_17Month_8 0.039 0.011 0.0082 0.00023 -0.0016 0.0043 -0.037 -0.0036 0.0021 0.0002 -0.0022 -0.0017 0.062 0.0019 0.062 0.036 0.00028 0.044 0.089 0.0072 -0.013 -0.00027 0.11 -0.00039 -0.0021 0.0094 -0.002 -0.00052 -0.00015 0.0017 0.00087 0.0044 -0.00099 -0.00072 -0.0012 -0.0018 0.013 0.0022 0.0049 0.017 0.013 0.00089 0.002 0.0012 -1.4e-05 -0.00051 6.1e-05 0.00018 -0.011 -0.0013 -0.00013 -8.2e-05 0.0012 0.00018 nan -0.00065 -0.00043 -0.0013 -0.0017 -0.0074 0.0012 -0.00011 -8.2e-05 -0.0001 -6.5e-05 -0.0001 -0.00081 -0.00035 -0.00018 -0.00078 -0.00053 -0.00052 -0.00069 -0.0004 -0.00052 -0.00049 -0.00054 -0.0011 -0.00059 -0.00066 -0.00098 -0.00069 -0.00098 -0.00012 -0.00016 -0.0041 -0.00075 -0.00016 -0.0021 -0.0011 1 -0.0057 -0.00056 -0.00075 -0.00072 -0.00033 -0.00064 -0.0023 -0.012 -0.0053 -0.0013 -0.0011 -0.0016 -0.0003 0.00041 -5.2e-06 0.00055 -0.0014 -0.0019 -7.3e-06 -0.0035 -0.0025
Interaction_MYYear_17Month_9 -0.033 0.069 0.033 -0.0047 -0.016 -0.0029 0.026 -0.034 0.0015 -0.0012 -0.0081 -0.0041 -0.056 0.15 -0.051 0.055 0.01 0.062 -0.018 -0.034 -0.058 -0.0091 0.14 -0.014 0.01 -0.011 0.034 -0.016 0.017 0.0068 -0.0052 0.0059 0.015 0.0073 0.022 0.0028 0.075 0.15 0.43 0.075 0.089 -0.019 -0.02 0.03 -0.0016 -0.0097 0.011 0.01 -0.037 -0.051 -0.0018 0.028 0.0066 -0.0014 nan -0.0044 0.035 0.0046 0.062 -0.081 0.0037 -0.0037 -0.0028 -0.0035 -0.0022 -0.0035 -0.028 -0.012 -0.0059 -0.026 -0.018 -0.018 -0.024 -0.014 -0.018 -0.017 -0.018 -0.037 -0.02 -0.022 -0.033 -0.023 -0.033 -0.004 -0.0055 -0.14 -0.025 -0.0053 -0.072 -0.036 -0.0057 1 -0.019 -0.025 -0.024 -0.011 -0.022 -0.076 -0.4 -0.18 -0.046 -0.038 -0.053 -0.01 -0.012 -0.00018 0.0079 -0.0072 -0.067 -0.00025 -0.051 0.028
Interaction_MYYear_17Month_10 -0.0044 0.02 0.04 -0.00098 -0.0011 0.0013 0.0039 -0.0059 0.0028 -0.00053 -0.00028 0.0012 0.014 0.004 -0.0071 0.039 0.022 0.042 0.08 -0.0074 -0.013 -0.00089 0.072 -0.0023 -0.0023 -0.0017 0.014 0.057 0.0015 -0.0018 0.0006 0.0033 6.8e-05 -1.9e-05 0.0087 0.002 0.08 0.004 -0.018 0.045 0.083 0.012 0.022 0.015 0.00064 -0.0021 0.027 -0.0061 -0.0021 -0.0083 -0.00042 0.0038 -0.0015 0.0081 nan 0.013 0.0061 0.0011 0.0085 -0.015 0.0039 -0.00037 -0.00027 -0.00035 -0.00021 -0.00034 -0.0027 -0.0012 -0.00058 -0.0026 -0.0018 -0.0017 -0.0023 -0.0013 -0.0017 -0.0016 -0.0018 -0.0036 -0.002 -0.0022 -0.0032 -0.0023 -0.0033 -0.00039 -0.00054 -0.014 -0.0025 -0.00052 -0.007 -0.0035 -0.00056 -0.019 1 -0.0025 -0.0024 -0.0011 -0.0021 -0.0075 -0.039 -0.018 -0.0044 -0.0037 -0.0052 -0.001 -0.0026 -1.7e-05 -0.006 0.00051 -0.0045 -2.5e-05 -0.0098 0.0024
Interaction_MYYear_17Month_11 -0.0055 0.028 0.05 0.0012 0.0033 0.0056 0.0052 -0.0068 0.0018 -0.00082 -0.00091 0.00069 -0.0059 0.017 -0.0091 0.052 0.018 0.057 0.13 -0.013 -0.019 -0.0012 0.097 -0.0021 0.0021 -0.0022 0.008 0.0096 0.0012 -0.0015 -0.00015 -0.0016 0.0002 0.0016 0.004 -0.00038 0.092 0.017 -0.024 0.061 0.095 -3.7e-05 0.0094 0.023 0.00084 -0.0028 0.02 -0.011 0.00029 -0.01 -0.00027 0.0051 -0.0022 0.0099 nan -0.00083 0.0079 1.7e-05 0.015 -0.015 0.0042 -0.00049 -0.00036 -0.00046 -0.00029 -0.00045 -0.0036 -0.0015 -0.00077 -0.0034 -0.0024 -0.0023 -0.0031 -0.0018 -0.0023 -0.0022 -0.0024 -0.0048 -0.0026 -0.0029 -0.0043 -0.0031 -0.0043 -0.00052 -0.00072 -0.018 -0.0033 -0.00069 -0.0093 -0.0047 -0.00075 -0.025 -0.0025 1 -0.0032 -0.0015 -0.0028 -0.01 -0.051 -0.023 -0.0059 -0.0049 -0.0069 -0.0013 -0.003 -2.3e-05 -0.015 0.0038 -0.013 -3.3e-05 -0.013 0.0042
Interaction_MYYear_17Month_12 0.01 0.021 0.031 -0.0024 -0.002 0.0035 -0.0096 -0.006 0.0015 -0.0024 0.00019 0.0024 -0.0016 0.012 -0.004 0.054 0.019 0.059 0.1 -0.013 -0.014 -0.0011 0.034 -0.0021 0.0026 0.00076 0.0096 0.0068 0.0018 -0.0036 -0.0027 -0.0053 0.0034 0.00075 0.0039 -0.0003 0.046 0.012 -0.022 0.059 0.047 -0.0039 0.0074 0.025 0.00059 -0.0016 0.021 -0.011 0.005 -0.01 -0.00054 0.0039 -0.0019 0.012 nan -0.0014 0.0087 -0.00078 0.018 -0.015 0.0021 -0.00047 -0.00035 -0.00044 -0.00027 -0.00043 -0.0035 -0.0015 -0.00074 -0.0033 -0.0023 -0.0022 -0.0029 -0.0017 -0.0022 -0.0021 -0.0023 -0.0047 -0.0025 -0.0028 -0.0042 -0.0029 -0.0042 -0.0005 -0.00069 -0.018 -0.0032 -0.00066 -0.009 -0.0045 -0.00072 -0.024 -0.0024 -0.0032 1 -0.0014 -0.0027 -0.0096 -0.049 -0.023 -0.0057 -0.0047 -0.0066 -0.0013 -0.0012 -2.2e-05 -0.0073 0.00093 -0.014 -3.2e-05 -0.014 0.0067
Interaction_MYYear_18Month_1 0.084 0.015 0.015 -0.00054 -0.0026 0.0069 -0.08 -0.0044 0.0014 0.00038 -0.0028 -0.00032 0.087 0.015 0.082 0.063 0.0013 0.074 0.099 0.022 -0.011 -0.00052 0.054 0.0015 -0.0012 0.016 0.002 0.014 0.0022 0.0041 0.001 0.0081 0.0036 4.3e-05 0.0015 0.0012 0.034 0.014 0.0057 0.035 0.034 0.0044 0.005 0.0037 0.0013 0.0013 0.0049 -0.0014 -0.014 -0.0028 -0.00025 0.0027 0.0035 -0.004 nan 0.0016 -0.00079 -0.0018 0.00063 -0.013 0.0043 -0.00022 -0.00016 -0.0002 -0.00013 -0.0002 -0.0016 -0.00068 -0.00034 -0.0015 -0.001 -0.001 -0.0014 -0.00079 -0.001 -0.00096 -0.001 -0.0021 -0.0012 -0.0013 -0.0019 -0.0013 -0.0019 -0.00023 -0.00032 -0.0081 -0.0015 -0.00031 -0.0041 -0.0021 -0.00033 -0.011 -0.0011 -0.0015 -0.0014 1 -0.0012 -0.0044 -0.023 -0.01 -0.0026 -0.0022 -0.003 -0.00059 0.0023 -1e-05 0.0015 -0.0022 -0.0046 -1.4e-05 -0.0064 -0.0026
Interaction_MYYear_18Month_2 -0.005 0.015 0.028 -0.0013 -0.002 9.4e-05 0.0043 -0.006 0.0015 -0.0011 -0.0013 0.00089 0.032 0.011 0.027 0.055 0.018 0.062 0.1 -0.0064 -0.015 -0.001 0.033 0.0014 0.00053 -0.0019 0.014 0.015 0.0024 -0.0029 -0.0019 -0.0017 0.0039 0.00013 0.0047 0.0031 0.07 0.011 -0.015 0.055 0.072 0.0049 0.013 0.021 0.00045 -0.0011 0.016 -0.0073 0.0039 -0.0076 0.0002 0.0036 -0.0017 0.012 nan 0.0015 0.011 0.00084 0.019 -0.016 0.004 -0.00042 -0.00031 -0.00039 -0.00024 -0.00039 -0.0031 -0.0013 -0.00066 -0.0029 -0.002 -0.002 -0.0026 -0.0015 -0.002 -0.0019 -0.002 -0.0041 -0.0022 -0.0025 -0.0037 -0.0026 -0.0037 -0.00044 -0.00061 -0.016 -0.0028 -0.00059 -0.008 -0.004 -0.00064 -0.022 -0.0021 -0.0028 -0.0027 -0.0012 1 -0.0085 -0.044 -0.02 -0.0051 -0.0042 -0.0059 -0.0011 0.0015 -2e-05 -0.0034 -0.00041 -0.012 -2.8e-05 -0.011 0.0068
Interaction_MYYear_18Month_3 0.0031 -0.0019 -0.002 0.00024 0.003 0.0037 -0.0029 -0.023 0.00097 -0.0033 -0.0061 0.00097 0.013 0.046 0.011 0.17 0.052 0.19 0.07 -0.044 -0.0021 -0.0036 0.095 -0.0067 0.0055 -0.0045 0.041 0.0049 0.016 -0.0092 -0.0014 -0.014 0.0046 -0.0047 0.009 0.012 0.043 0.046 -0.067 0.19 0.049 -0.0016 0.028 0.081 -0.00085 -0.012 0.019 -0.015 0.023 -0.031 -0.001 0.01 -0.0024 0.032 nan -0.0028 0.027 -0.0033 0.067 -0.023 -0.0015 -0.0015 -0.0011 -0.0014 -0.00086 -0.0014 -0.011 -0.0047 -0.0023 -0.01 -0.0071 -0.007 -0.0093 -0.0054 -0.0069 -0.0066 -0.0072 -0.015 -0.0079 -0.0088 -0.013 -0.0092 -0.013 -0.0016 -0.0022 -0.055 -0.01 -0.0021 -0.028 -0.014 -0.0023 -0.076 -0.0075 -0.01 -0.0096 -0.0044 -0.0085 1 -0.16 -0.071 -0.018 -0.015 -0.021 -0.0041 -0.0097 -6.9e-05 -0.02 0.0055 -0.037 -9.9e-05 -0.034 0.022
Interaction_MYYear_18Month_4 -0.084 -0.12 -0.29 0.011 0.028 -0.02 0.085 0.12 -0.018 0.0097 0.046 0.036 -0.14 -0.074 -0.13 -0.59 -0.12 -0.63 -0.33 0.058 0.12 0.023 -0.47 0.018 -0.014 -0.0045 -0.012 -0.018 -0.017 0.016 -0.028 0.013 0.037 -1.6e-06 -0.037 0.069 -0.5 -0.074 -0.72 -0.58 -0.5 -0.038 -0.09 -0.18 0.021 0.052 -0.074 -0.013 -0.024 1.6e-05 -0.0043 -0.043 0.022 -0.1 nan 0.01 -0.042 0.017 -0.064 0.14 -0.035 -0.0076 -0.0057 -0.0072 -0.0045 -0.0071 -0.056 -0.024 -0.012 -0.054 -0.037 -0.036 -0.048 -0.028 -0.036 -0.034 -0.037 -0.076 -0.041 -0.045 -0.068 -0.048 -0.068 -0.0081 -0.011 -0.29 -0.052 -0.011 -0.15 -0.074 -0.012 -0.4 -0.039 -0.051 -0.049 -0.023 -0.044 -0.16 1 -0.37 -0.093 -0.076 -0.11 -0.021 0.0026 0.00045 0.052 -0.013 0.11 0.00063 0.15 -0.028
Interaction_MYYear_18Month_5 -0.022 -0.041 -0.11 0.01 0.012 0.013 0.021 -0.062 0.015 -0.0017 -0.0019 -0.0046 -0.038 -0.13 -0.032 -0.037 0.088 -0.041 -0.12 -0.087 0.023 -0.0084 -0.093 0.013 0.011 -0.0072 -0.065 -0.016 -0.024 -0.016 0.033 -0.027 -0.08 0.015 -0.00032 -0.086 -0.0051 -0.13 -0.00087 -0.032 0.0042 0.0082 0.047 0.0092 0.023 0.0009 0.1 -0.065 -0.014 -0.054 -0.0014 0.0025 -0.029 0.099 nan -0.018 -0.0082 -0.0037 -0.05 0.0034 0.015 -0.0035 -0.0026 -0.0033 -0.002 -0.0032 -0.026 -0.011 -0.0055 -0.024 -0.017 -0.017 -0.022 -0.013 -0.016 -0.015 -0.017 -0.035 -0.019 -0.021 -0.031 -0.022 -0.031 -0.0037 -0.0051 -0.13 -0.023 -0.0049 -0.067 -0.034 -0.0053 -0.18 -0.018 -0.023 -0.023 -0.01 -0.02 -0.071 -0.37 1 -0.042 -0.035 -0.049 -0.0096 0.025 -0.00016 -0.065 0.032 0.0094 -0.00023 -0.028 -0.0017
Interaction_MYYear_18Month_6 0.25 -0.042 0.043 -0.0025 -0.013 0.014 -0.24 -0.03 0.012 -0.0004 -0.019 -0.009 0.48 0.013 0.46 0.29 0.011 0.34 0.085 0.074 0.0018 -0.0019 0.1 0.0032 -0.0077 0.043 0.0048 0.049 0.0047 0.015 0.0055 0.036 -0.0022 -0.0014 0.0053 0.0029 0.12 0.014 0.047 0.15 0.12 0.03 0.031 0.003 0.0017 0.0026 0.0095 -0.0052 -0.081 0.025 0.013 0.0042 0.011 0.00058 nan 0.0062 0.00049 -0.0061 -0.01 -0.054 0.023 -0.00088 -0.00065 -0.00083 -0.00051 -0.00081 -0.0065 -0.0028 -0.0014 -0.0062 -0.0042 -0.0042 -0.0055 -0.0032 -0.0041 -0.0039 -0.0043 -0.0087 -0.0047 -0.0052 -0.0078 -0.0055 -0.0078 -0.00093 -0.0013 -0.033 -0.0059 -0.0012 -0.017 -0.0085 -0.0013 -0.046 -0.0044 -0.0059 -0.0057 -0.0026 -0.0051 -0.018 -0.093 -0.042 1 -0.0088 -0.012 -0.0024 0.0062 -4.1e-05 0.0095 -0.011 -0.0045 -5.8e-05 -0.019 -0.02
Interaction_MYYear_18Month_7 -0.0086 -0.011 0.028 0.0015 -0.011 0.0054 0.003 -0.023 0.0038 0.00047 -0.012 -0.0073 0.49 -0.012 0.46 0.22 0.027 0.27 0.1 0.0068 0.0005 -0.0017 0.018 0.012 -0.0098 0.0017 0.0083 0.073 0.00049 0.0046 0.0055 0.029 -0.015 -0.0016 0.0063 -0.0025 0.092 -0.01 0.03 0.11 0.09 0.038 0.049 0.0047 0.0036 -0.0015 0.014 -0.00094 -0.068 -0.00051 0.0029 -0.00016 0.0032 0.032 nan 0.0096 0.005 -0.0055 -0.0096 -0.046 0.0077 -0.00072 -0.00054 -0.00069 -0.00042 -0.00067 -0.0053 -0.0023 -0.0012 -0.0051 -0.0035 -0.0034 -0.0046 -0.0026 -0.0034 -0.0032 -0.0035 -0.0072 -0.0039 -0.0043 -0.0064 -0.0045 -0.0064 -0.00077 -0.0011 -0.027 -0.0049 -0.001 -0.014 -0.007 -0.0011 -0.038 -0.0037 -0.0049 -0.0047 -0.0022 -0.0042 -0.015 -0.076 -0.035 -0.0088 1 -0.01 -0.002 0.016 -3.4e-05 0.0018 -0.0091 0.0015 -4.8e-05 -0.015 -0.011
Interaction_MYYear_18Month_8 0.32 0.011 0.0096 -0.0018 -0.0052 0.012 -0.3 -0.028 0.013 0.0018 -0.019 -0.014 0.21 0.023 0.18 0.25 -0.0077 0.22 0.21 0.13 0.02 -0.0016 0.16 0.0028 -0.008 0.066 0.015 0.077 0.013 0.021 0.0048 0.036 0.024 -0.0044 0.0071 0.025 0.19 0.023 0.085 0.15 0.18 0.062 0.031 0.0093 0.0036 0.0024 0.00057 -0.0034 -0.041 0.086 0.017 0.0047 0.019 -0.033 nan 0.017 -0.0097 -0.0066 -0.013 -0.016 0.03 -0.001 -0.00076 -0.00097 -0.0006 -0.00095 -0.0075 -0.0032 -0.0016 -0.0072 -0.0049 -0.0049 -0.0064 -0.0037 -0.0048 -0.0045 -0.005 -0.01 -0.0055 -0.0061 -0.0091 -0.0064 -0.0091 -0.0011 -0.0015 -0.038 -0.0069 -0.0015 -0.02 -0.0099 -0.0016 -0.053 -0.0052 -0.0069 -0.0066 -0.003 -0.0059 -0.021 -0.11 -0.049 -0.012 -0.01 1 -0.0028 -0.012 -4.8e-05 0.018 -0.0086 0.0037 -6.8e-05 -0.0012 -0.022
Interaction_MYYear_18Month_9 -0.0024 0.012 0.026 -0.00016 0.00029 -0.00041 0.0026 -0.0094 0.0044 0.00013 -0.0096 -0.0097 -0.0042 -0.0023 -0.0039 0.15 -0.017 0.061 0.14 0.042 0.0052 0.00051 0.16 0.0012 -0.00058 0.0028 0.0014 -0.00094 0.00092 0.0028 0.0017 0.007 0.01 0.0026 -0.00096 0.0062 0.11 -0.0024 0.067 0.14 0.024 0.029 0.0015 -0.021 -0.00025 -0.0017 -0.0071 0.00096 -0.0012 0.17 0.021 -0.0027 0.0049 -0.011 nan 0.0077 -0.00089 0.0017 -0.0016 0.011 0.01 -0.0002 -0.00015 -0.00019 -0.00012 -0.00018 -0.0015 -0.00063 -0.00032 -0.0014 -0.00096 -0.00095 -0.0013 -0.00073 -0.00093 -0.00089 -0.00097 -0.002 -0.0011 -0.0012 -0.0018 -0.0012 -0.0018 -0.00021 -0.00029 -0.0075 -0.0013 -0.00028 -0.0038 -0.0019 -0.0003 -0.01 -0.001 -0.0013 -0.0013 -0.00059 -0.0011 -0.0041 -0.021 -0.0096 -0.0024 -0.002 -0.0028 1 -0.0015 -9.4e-06 0.0063 -0.0019 0.01 -1.3e-05 0.012 -0.00097
Device_PossibleOwnership -0.031 -0.021 -0.0028 0.016 0.022 -0.0073 0.03 0.021 -0.026 0.001 0.019 0.022 0.023 -0.053 -0.00044 -0.0063 -0.026 -0.0055 -0.0096 0.05 -0.01 -0.0049 -0.015 0.8 -0.03 -0.004 -0.022 0.066 -0.015 -0.017 -0.0076 -0.014 -0.033 0.037 -0.0021 -0.028 -0.0057 -0.052 -0.012 0.00073 -0.0054 0.072 0.029 -0.094 0.0013 -0.014 0.022 -0.013 0.049 0.015 0.0029 -0.0063 -0.017 -0.0076 nan -0.0019 -0.014 -0.012 -0.041 -0.011 -0.073 -0.0017 -0.0014 -0.00074 -0.00033 -0.0014 0.012 0.0064 0.0012 -0.00048 0.0048 -0.0031 -0.0019 -0.0028 -0.0062 -0.0039 -0.0042 -0.001 -0.0039 -0.0044 -0.0082 -0.0025 -0.0065 -0.00036 -0.00011 -0.022 -0.0035 -0.0019 0.029 -0.0066 0.00041 -0.012 -0.0026 -0.003 -0.0012 0.0023 0.0015 -0.0097 0.0026 0.025 0.0062 0.016 -0.012 -0.0015 1 -0.00017 0.03 -0.023 0.021 0.00053 -0.017 -0.018
AV_highrisk -4.1e-05 -2.9e-05 -0.00023 0.0048 -5.3e-05 0.0067 4.4e-05 -0.00024 0.00046 0.00023 -0.0003 -0.00011 -7.2e-05 -0.00013 -6.7e-05 -0.00027 0.0003 -0.0003 -0.00016 -0.00026 9.6e-05 -8.3e-06 -0.00022 -0.00014 5.9e-05 -3.1e-05 -0.00022 -1.6e-05 -5.7e-05 -5.4e-05 -0.00015 -0.00023 -0.00025 -0.00012 -0.00012 -0.00022 -0.00029 -0.00013 -0.00033 -0.00028 -0.0003 -0.00033 -7.5e-06 0.00067 -0.00017 0.0003 -2.1e-05 -0.00014 -0.00036 -9.1e-05 -3.9e-06 -0.00017 7.7e-05 0.00041 nan -3.4e-05 -0.00015 -8e-05 -9.8e-05 -0.00025 -0.00045 -3.4e-06 -2.5e-06 -3.2e-06 -2e-06 -3.2e-06 -2.5e-05 -1.1e-05 -5.4e-06 -2.4e-05 -1.6e-05 -1.6e-05 -2.1e-05 -1.2e-05 -1.6e-05 -1.5e-05 -1.7e-05 -3.4e-05 -1.8e-05 -2e-05 -3e-05 -2.1e-05 -3e-05 -3.6e-06 -5e-06 -0.00013 -2.3e-05 -4.8e-06 -6.5e-05 -3.3e-05 -5.2e-06 -0.00018 -1.7e-05 -2.3e-05 -2.2e-05 -1e-05 -2e-05 -6.9e-05 0.00045 -0.00016 -4.1e-05 -3.4e-05 -4.8e-05 -9.4e-06 -0.00017 1 -0.00061 -5.4e-05 -0.00026 -2.1e-07 -0.00019 -0.00015
AV_mediumrisk 0.022 -0.09 -0.099 -0.14 -0.2 -0.17 -0.026 -0.048 0.0039 0.012 -0.066 -0.036 0.013 0.05 0.0031 -0.0042 -0.17 -0.0096 0.0069 0.16 0.047 0.013 0.0015 0.027 0.0031 0.0082 0.087 0.026 0.056 0.043 -0.023 0.071 0.11 -0.027 0.036 0.11 -0.02 0.05 -0.026 -0.012 -0.028 0.011 -0.084 0.013 -0.022 0.0091 -0.13 0.12 -0.12 0.036 0.003 -0.002 0.068 -0.21 nan 0.05 0.011 0.029 0.07 -0.018 0.024 0.0033 0.0019 0.0033 0.001 0.0015 0.021 0.0042 -0.00011 0.02 0.0037 0.013 0.0022 -0.0024 0.0014 0.00084 0.011 0.02 -0.005 -0.012 -0.01 0.00099 -0.013 0.0032 0.0015 -0.028 -0.01 0.00033 0.014 -0.015 0.00055 0.0079 -0.006 -0.015 -0.0073 0.0015 -0.0034 -0.02 0.052 -0.065 0.0095 0.0018 0.018 0.0063 0.03 -0.00061 1 -0.21 -0.0037 -0.00024 0.055 0.026
AV_lowrisk -0.01 0.03 0.026 0.084 0.044 0.25 0.011 0.011 0.0023 -0.0042 0.017 0.0064 -0.02 -0.021 -0.018 -0.013 0.064 -0.014 -0.014 -0.057 -0.038 -0.0026 -0.011 -0.017 0.0064 -0.0033 -0.03 -0.0054 -0.017 -0.017 0.012 -0.026 -0.039 0.0043 -0.0098 -0.039 -0.0023 -0.021 0.0022 -0.0071 -1.2e-06 0.001 0.036 0.00018 0.0084 0.0016 0.044 -0.034 0.058 -0.012 -0.00083 -0.00044 -0.022 0.08 nan -0.011 -0.0087 -0.0092 -0.021 0.022 -0.0076 -0.0012 -0.00086 -0.0011 -0.00068 -0.0011 -0.0074 -0.002 -0.001 -0.0061 -0.0036 -0.0038 -0.0028 -0.0021 -0.0025 -0.0024 -0.0036 -0.0061 -0.0013 -0.00032 0.00058 -0.0011 0.0028 -0.00096 -0.0013 0.012 0.0013 -0.001 -0.01 0.0012 -0.0014 -0.0072 0.00051 0.0038 0.00093 -0.0022 -0.00041 0.0055 -0.013 0.032 -0.011 -0.0091 -0.0086 -0.0019 -0.023 -5.4e-05 -0.21 1 0.0057 -7.8e-05 -0.017 -0.0069
Interaction_01 -0.014 -0.036 -0.064 0.011 0.017 0.0027 0.012 0.019 0.026 -0.00064 0.027 0.019 0.0079 -0.19 -0.015 -0.091 -0.053 -0.1 -0.085 0.056 0.037 0.0015 -0.085 0.033 -0.014 0.0034 -0.11 0.062 -0.051 0.0036 0.14 0.1 0.042 0.015 -0.036 -0.034 -0.07 -0.19 -0.11 -0.097 -0.073 -0.0097 -0.031 -0.053 0.053 0.038 0.01 -0.0025 -0.016 0.00046 0.003 -0.027 -0.0047 -0.0091 nan 0.08 -0.07 -0.012 -0.2 0.067 0.034 -0.0014 -0.00074 -0.0015 -0.00067 -0.002 -0.015 -0.0085 -0.0064 -0.0099 -0.017 -0.0073 -0.012 -0.0096 -0.0089 -0.0062 -0.0058 -0.0082 -0.011 -0.009 -0.023 -0.0085 -0.0075 -0.0054 -0.0016 -0.026 -0.013 -0.0019 -0.051 -0.021 -0.0019 -0.067 -0.0045 -0.013 -0.014 -0.0046 -0.012 -0.037 0.11 0.0094 -0.0045 0.0015 0.0037 0.01 0.021 -0.00026 -0.0037 0.0057 1 0.0003 0.16 -0.063
Interaction_02 -5.9e-05 -4.1e-05 -8.3e-05 -8.1e-05 -7.5e-05 -6.5e-05 6.3e-05 5.4e-05 0.00033 -0.00015 -0.00045 -0.00042 0.0016 -0.00018 0.0016 0.00051 0.00042 0.00072 -8.8e-05 -0.00037 0.00013 -1.2e-05 4.4e-06 0.0002 8.1e-05 -4.3e-05 -0.00031 -2.2e-05 -9.5e-05 -0.00014 0.00067 0.00034 -0.00036 0.0009 -0.00017 -0.00031 0.00022 -0.00018 -0.00012 0.00027 0.00024 -0.00046 -1e-05 -0.0003 -0.00049 -0.00042 -3.3e-05 -0.00019 -0.00051 -0.00013 -5.5e-06 0.00014 -0.00024 9.3e-06 nan -5e-05 -0.00022 -0.00012 -0.00015 -0.00035 0.00033 -4.8e-06 -3.4e-06 -4.5e-06 -2.8e-06 -4.4e-06 -3.4e-05 -1.5e-05 -7.6e-06 -3.3e-05 -2.3e-05 -2.3e-05 -3e-05 -1.8e-05 -2.3e-05 -2.1e-05 -2.3e-05 -4.7e-05 -2.6e-05 -2.9e-05 -4.3e-05 -3e-05 -4.3e-05 -5.2e-06 -6.9e-06 -0.00018 -3.3e-05 -6.8e-06 -9e-05 -4.7e-05 -7.3e-06 -0.00025 -2.5e-05 -3.3e-05 -3.2e-05 -1.4e-05 -2.8e-05 -9.9e-05 0.00063 -0.00023 -5.8e-05 -4.8e-05 -6.8e-05 -1.3e-05 0.00053 -2.1e-07 -0.00024 -7.8e-05 0.0003 1 -0.00027 -0.00021
Interaction_03 -0.027 -0.045 -0.083 0.005 0.018 -0.0043 0.025 -0.015 0.015 0.023 -0.003 -0.016 -0.036 -0.11 -0.037 -0.11 -0.099 -0.13 -0.086 0.071 0.052 0.0028 -0.094 0.0052 -0.007 0.0054 0.2 0.0013 0.015 0.025 -0.057 0.04 0.18 -0.013 0.088 0.2 -0.083 -0.11 -0.11 -0.11 -0.085 -0.026 -0.057 -0.03 0.028 0.041 0.0083 -0.031 0.08 0.01 0.0028 -0.016 0.062 0.033 nan 0.04 0.18 0.3 0.16 0.08 0.1 -0.00016 -0.00046 -0.0021 -0.00055 -0.0028 -0.022 -0.0093 -0.0049 -0.016 -0.015 -0.0094 -0.014 -0.009 -0.012 -0.011 -0.013 -0.018 -0.014 -0.016 -0.021 -0.011 -0.016 -0.00019 -0.0031 -0.031 -0.015 -0.0033 -0.052 -0.023 -0.0035 -0.051 -0.0098 -0.013 -0.014 -0.0064 -0.011 -0.034 0.15 -0.028 -0.019 -0.015 -0.0012 0.012 -0.017 -0.00019 0.055 -0.017 0.16 -0.00027 1 0.56
PPI -0.04 -0.0067 -0.011 -0.0045 0.0033 -0.011 0.039 -0.013 -0.02 0.015 0.0077 -0.02 -0.033 0.0088 -0.027 -0.0029 0.074 -0.0054 0.0056 -0.076 0.0043 0.019 0.0034 -0.017 0.03 -0.017 0.28 -0.019 0.04 -0.014 -0.14 -0.1 0.11 -0.055 0.22 0.011 0.026 0.0089 0.035 0.011 0.029 -0.02 0.036 0.072 -0.018 -0.016 0.034 -0.066 0.2 0.0064 0.00015 0.052 0.058 0.22 nan -0.0036 0.45 0.44 0.47 -0.035 0.046 0.00089 0.00046 -0.0001 0.00027 -0.0021 -0.014 -0.0021 0.0022 -0.0092 -0.0002 -0.0043 -0.0016 0.00083 -0.0037 -0.0011 -0.006 -0.0073 0.0019 -0.0013 0.0076 -0.0009 -0.0012 0.0063 -0.0024 0.028 0.0014 -0.0026 -0.0086 0.0074 -0.0025 0.028 0.0024 0.0042 0.0067 -0.0026 0.0068 0.022 -0.028 -0.0017 -0.02 -0.011 -0.022 -0.00097 -0.018 -0.00015 0.026 -0.0069 -0.063 -0.00021 0.56 1
In [170]:
#X.drop(columns=['HasTpm','AV_mediumrisk','AV_update_status''Interaction_P_OsVer_01','Interaction_P_OsVer_02','Interaction_P_OsVer_03'],inplace=True)
#df_test.drop(columns=['HasTpm','AV_mediumrisk','AV_update_status''Interaction_P_OsVer_01','Interaction_P_OsVer_02','Interaction_P_OsVer_03'],inplace=True)
In [171]:
X[['Census_OSBuildRevision','Census_OSSkuName']].head()
Out[171]:
Census_OSBuildRevision Census_OSSkuName
0 0.032027 -0.018806
1 -0.610708 0.797394
2 -0.707118 -0.018806
3 -0.674982 2.429792
4 -0.578571 -0.018806
In [172]:
#df.drop(columns=['OsSuite','OsBuild','OsVer','Census_OSArchitecture','Census_OSBuildRevision','Census_OSSkuName'],inplace=True)
#df_test.drop(columns=['OsSuite','OsBuild','OsVer','Census_OSArchitecture','Census_OSBuildRevision','Census_OSSkuName'],inplace=True)
In [173]:
#X.drop(columns=['OsSuite','OsBuild','OsVer','Census_OSArchitecture','Census_OSBuildRevision','Census_OSSkuName'],inplace=True)
In [174]:
df_test.drop(columns=['Census_OSBuildRevision','Census_OSSkuName'],inplace=True)
X.drop(columns=['Census_OSBuildRevision','Census_OSSkuName'],inplace=True)
In [175]:
%%time
for col in X.columns:
    if X[col].nunique()==1:
        print(col)
        X.drop(columns=[col],inplace=True)
        df_test.drop(columns=[col],inplace=True)
    else:
        continue
Census_IsWIMBootEnabled
Wall time: 39.5 s
In [176]:
%%time
fig,ax=plt.subplots(figsize=(12,12))
corr=X.corr()

# plot the heatmap
sns.heatmap(corr,xticklabels=corr.columns,yticklabels=corr.columns,ax=ax)
Wall time: 3min 6s
In [177]:
%%time
cmap=sns.diverging_palette(5, 250, as_cmap=True)
def magnify():
    return [dict(selector="th",
                 props=[("font-size", "7pt")]),
            dict(selector="td",
                 props=[('padding', "0em 0em")]),
            dict(selector="th:hover",
                 props=[("font-size", "12pt")]),
            dict(selector="tr:hover td:hover",
                 props=[('max-width', '200px'),
                        ('font-size', '12pt')])
]

corr.style.background_gradient(cmap, axis=1)\
    .set_properties(**{'max-width': '80px', 'font-size': '10pt'})\
    .set_caption("Hover to magify")\
    .set_precision(2)\
    .set_table_styles(magnify())
Wall time: 40.9 ms
In [178]:
%%time
#X.drop(columns=['HasTpm','IeVerIdentifier','AV_update_status','Interaction_03','LocaleEnglishNameIdentifier','OsPlatformSubRelease','IsProtected','Census_OSVersion','Census_MDC2FormFactor','Device_PossibleOwnership','Census_OSBuildNumber','Platform','Census_OSInstallLanguageIdentifier'],inplace=True)
#df_test.drop(columns=['HasTpm','IeVerIdentifier','AV_update_status','Interaction_03','LocaleEnglishNameIdentifier','OsPlatformSubRelease','IsProtected','Census_OSVersion','Census_MDC2FormFactor','Device_PossibleOwnership','Census_OSBuildNumber','Platform','Census_OSInstallLanguageIdentifier'],inplace=True)
Wall time: 0 ns
In [179]:
#X.drop(columns=['IeVerIdentifier_encode','Census_MDC2FormFactor_encode'],inplace=True)
#df_test.drop(columns=['IeVerIdentifier_encode','Census_MDC2FormFactor_encode'],inplace=True)
In [180]:
X.shape
Out[180]:
(6244507, 109)
In [181]:
from sklearn.model_selection import train_test_split
In [201]:
X_train,X_test,y_train,y_test=train_test_split(X.values,y.values,test_size=0.3,random_state=42)
In [200]:
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1)
In [202]:
from sklearn.metrics import accuracy_score,precision_score,recall_score,confusion_matrix,roc_auc_score,roc_curve
from sklearn.tree import DecisionTreeClassifier
In [203]:
clf1 = DecisionTreeClassifier(criterion='entropy', max_depth=5)
clf2 = KNeighborsClassifier(n_neighbors=5)    

bagging1 = BaggingClassifier(base_estimator=clf1, n_estimators=10, max_samples=0.8, max_features=0.9)
bagging2 = BaggingClassifier(base_estimator=clf2, n_estimators=10, max_samples=0.8, max_features=0.9)
In [204]:
import itertools
In [208]:
%%time
bagging1.fit(X_train,y_train)
Wall time: 12min 47s
Out[208]:
BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=5,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best'),
         bootstrap=True, bootstrap_features=False, max_features=0.9,
         max_samples=0.8, n_estimators=10, n_jobs=1, oob_score=False,
         random_state=None, verbose=0, warm_start=False)
In [211]:
predictions=bagging1.predict(X_test)
In [212]:
prediction_scores=bagging1.predict_proba(X_test)[:,1]
In [213]:
print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,prediction_scores))
Confusion Matrix for the Model : 
 [[459220 478785]
 [245312 690036]]
Accuracy of the model :  0.6134754101335946
Precision of the Model :  0.5903692695459783
Recall score of the Model :  0.7377318388450074
Area under ROC curve for the Model :  0.6683681213065038
In [ ]:
%%time
#bagging2.fit(X_train,y_train)
In [ ]:
#predictions=bagging2.predict(X_test)
In [ ]:
#prediction_scores=bagging2.predict_proba(X_test)[:,1]
In [ ]:
print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,prediction_scores))
In [187]:
from lightgbm import LGBMClassifier
In [189]:
clf=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15 ,max_depth=-1,
        min_child_samples=50, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=100, n_jobs=-1, num_leaves=500, objective=None,
        random_state=42, reg_alpha=0, reg_lambda=0.0, silent=False,
        subsample=0.9,subsample_freq=1)
In [190]:
%%time
clf.fit(X_train,y_train)
Wall time: 1min 55s
Out[190]:
LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15, max_depth=-1,
        min_child_samples=50, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=100, n_jobs=-1, num_leaves=500, objective=None,
        random_state=42, reg_alpha=0, reg_lambda=0.0, silent=False,
        subsample=0.9, subsample_for_bin=200000, subsample_freq=1)
In [191]:
predictions=clf.predict(X_test)
prediction_scores=clf.predict_proba(X_test)
scores=[]
for score in prediction_scores:
    scores.append(score[1])
C:\Users\gandh\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:
In [192]:
print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,scores))
Confusion Matrix for the Model : 
 [[447455 221472]
 [231852 437330]]
Accuracy of the model :  0.6612204237472433
Precision of the Model :  0.6638261571762077
Recall score of the Model :  0.6535292341993658
Area under ROC curve for the Model :  0.7264697348603679
In [195]:
prediction_scores=clf.predict_proba(df_test[X.columns])
scores=[]
for score in prediction_scores:
    scores.append(score[1])
In [196]:
%%time
solution['HasDetections']=scores
solution.to_csv('submit_31st.csv',index=False)
Wall time: 1min 4s
In [197]:
solution.head()
Out[197]:
MachineIdentifier HasDetections
0 0000010489e3af074adeac69c53e555e 0.599641
1 00000176ac758d54827acd545b6315a5 0.748345
2 0000019dcefc128c2d4387c1273dae1d 0.454333
3 0000055553dc51b1295785415f1a224d 0.469111
4 00000574cefffeca83ec8adf9285b2bf 0.662187
In [198]:
temp=pd.DataFrame()

temp['features']=X_train.columns[np.argsort(clf.feature_importances_)]

temp['importance']=np.sort(clf.feature_importances_)

temp.sort_values(by=['importance'],ascending=False,inplace=True)
In [199]:
temp
Out[199]:
features importance
108 SmartScreen 402944.766618
107 AVProductStatesIdentifier 280888.551537
106 EngineVersion 89252.064796
105 AppVersion 80002.517550
104 CountryIdentifier 54711.030009
103 Interaction_02 50108.271050
102 Census_OSInstallTypeName 49610.878160
101 Device_PossibleOwnership 47137.113151
100 Census_OSVersion 38533.994658
99 Wdft_IsGamer 37674.048997
98 PPI 31586.841548
97 Wdft_RegionIdentifier 30702.778116
96 Census_ProcessorModelIdentifier 30316.556373
95 CityIdentifier 29678.426420
94 Interaction_03 27916.432337
93 LocaleEnglishNameIdentifier 23723.240506
92 Census_FirmwareVersionIdentifier 23687.421844
91 Census_IsAlwaysOnAlwaysConnectedCapable 23577.176166
90 Census_OSInstallLanguageIdentifier 23491.129490
89 Census_OEMModelIdentifier 23072.982218
88 OsBuildLab 22961.664022
87 Census_ActivationChannel 22429.254086
86 Census_IsVirtualDevice 21808.445312
85 GeoNameIdentifier 19510.442236
84 Census_OEMNameIdentifier 18945.871079
83 Census_OSUILocaleIdentifier 17960.874937
82 IeVerIdentifier 16503.889473
81 RtpStateBitfield 16277.195962
80 Processor 16081.757362
79 Census_OSEdition 15835.934268
... ... ...
29 Interaction_MYYear_16Month_5 163.782850
28 Interaction_MYYear_17Month_4 124.332320
27 Interaction_MYYear_17Month_12 120.278489
26 Interaction_MYYear_16Month_10 102.264990
25 Interaction_MYYear_18Month_2 95.210730
24 Interaction_MYYear_16Month_2 90.008140
23 Interaction_MYYear_16Month_9 87.078200
22 Interaction_MYYear_16Month_4 85.361409
21 Interaction_MYYear_16Month_8 79.674981
20 Interaction_MYYear_17Month_11 62.202849
19 Interaction_MYYear_15Month_11 45.983190
18 Interaction_MYYear_17Month_10 39.600840
17 Interaction_MYYear_16Month_3 30.422991
16 Interaction_MYYear_18Month_9 29.952830
15 Interaction_MYYear_16Month_1 19.081050
14 Interaction_MYYear_17Month_1 7.404160
13 Interaction_MYYear_15Month_3 6.556640
12 Interaction_MYYear_17Month_5 6.225560
11 Interaction_MYYear_17Month_8 5.517140
3 Interaction_MYYear_14Month_7 0.000000
4 Interaction_MYYear_17Month_2 0.000000
6 Interaction_MYYear_14Month_10 0.000000
2 Interaction_MYYear_14Month_3 0.000000
5 Interaction_MYYear_18Month_1 0.000000
1 AV_highrisk 0.000000
7 Interaction_MYYear_15Month_9 0.000000
8 Interaction_MYYear_15Month_8 0.000000
9 Interaction_MYYear_15Month_1 0.000000
10 Census_DeviceFamily 0.000000
0 Census_ThresholdOptIn 0.000000

109 rows × 2 columns

In [200]:
important_columns=temp[temp.importance>0]['features'].tolist()
In [201]:
from sklearn.feature_selection import RFE
In [202]:
#selector = RFE(clf, 75, step=1,verbose=True)
In [203]:
%%time
#selector=selector.fit(X_train, y_train)
Wall time: 0 ns
In [204]:
#print(selector.support_)
In [205]:
#print(selector.ranking_)
In [226]:
clf2=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15, max_depth=-1,
        min_child_samples=2, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=500, n_jobs=-1, num_leaves=500, objective=None,
        random_state=42, reg_alpha=0.7, reg_lambda=0.7, silent=False,
        subsample=0.9, subsample_for_bin=100000, subsample_freq=1)
In [227]:
%%time
clf2.fit(X_train[important_columns],y_train)
Wall time: 4min 28s
Out[227]:
LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15, max_depth=-1,
        min_child_samples=2, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=500, n_jobs=-1, num_leaves=500, objective=None,
        random_state=42, reg_alpha=0.7, reg_lambda=0.7, silent=False,
        subsample=0.9, subsample_for_bin=100000, subsample_freq=1)
In [228]:
predictions=clf2.predict(X_test[important_columns])
prediction_scores=clf2.predict_proba(X_test[important_columns])
scores=[]
for score in prediction_scores:
    scores.append(score[1])
C:\Users\gandh\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:
In [230]:
print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,scores))
Confusion Matrix for the Model : 
 [[444490 224437]
 [230343 438839]]
Accuracy of the model :  0.6601323210590467
Precision of the Model :  0.6616235172085225
Recall score of the Model :  0.6557842261148686
Area under ROC curve for the Model :  0.7247767926123824
In [231]:
prediction_scores=clf2.predict_proba(df_test[important_columns])
scores=[]
for score in prediction_scores:
    scores.append(score[1])
In [276]:
%%time
solution['HasDetections']=scores
solution.to_csv('submit_31st.csv',index=False)
Wall time: 30.8 s
In [233]:
clf3=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15, max_depth=-1,
        min_child_samples=2, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=1000, n_jobs=-1, num_leaves=100, objective=None,
        random_state=42, reg_alpha=0.9, reg_lambda=0.9, silent=False,
        subsample=0.9, subsample_for_bin=200000, subsample_freq=1)
In [235]:
%%time
clf2.fit(X_train[important_columns],y_train)
Wall time: 5min 15s
Out[235]:
LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='gain', learning_rate=0.15, max_depth=-1,
        min_child_samples=2, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=500, n_jobs=-1, num_leaves=500, objective=None,
        random_state=42, reg_alpha=0.7, reg_lambda=0.7, silent=False,
        subsample=0.9, subsample_for_bin=100000, subsample_freq=1)
In [236]:
predictions=clf2.predict(X_test[important_columns])
prediction_scores=clf2.predict_proba(X_test[important_columns])
scores=[]
for score in prediction_scores:
    scores.append(score[1])
C:\Users\gandh\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:
In [238]:
print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,scores))
Confusion Matrix for the Model : 
 [[444490 224437]
 [230343 438839]]
Accuracy of the model :  0.6601323210590467
Precision of the Model :  0.6616235172085225
Recall score of the Model :  0.6557842261148686
Area under ROC curve for the Model :  0.7247767926123824
In [239]:
#clf=DecisionTreeClassifier(random_state=42)
In [240]:
import keras
Using TensorFlow backend.
In [241]:
from keras.models import Sequential
from keras.layers import Dense
from keras import layers
# Cross-validation
from sklearn.model_selection import KFold, StratifiedKFold, KFold #for K-fold cross validation
from sklearn.model_selection import cross_val_score #score evaluation
from sklearn.model_selection import cross_val_predict #prediction
from sklearn.model_selection import cross_validate
from keras.callbacks import EarlyStopping, ModelCheckpoint
# fix random seed for reproducibility
np.random.seed(7)
In [242]:
len(important_columns)
Out[242]:
98
In [243]:
X.shape
Out[243]:
(4460362, 109)
In [244]:
classifier = Sequential()
#First Hidden Layer
classifier.add(layers.Dropout(0.5, input_shape=(109,)),)
#Second  Hidden Layer
classifier.add(layers.Dense(units=20, activation='relu'))
# Add a dropout layer for previous hidden layer
classifier.add(layers.Dropout(0.5))
#classifier.add(layers.Dense(units=20, activation='relu'))
#classifier.add(layers.MaxPool1D(pool_size=4,data_format='channels_first'))
#classifier.add(layers.Dropout(0.5))
#classifier.add(layers.Dense(units=10, activation='relu'))
#classifier.add(layers.Dropout(0.5))
#Output Layer
classifier.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal'))
In [245]:
#Compiling the neural network
classifier.compile(optimizer ='adam',loss='binary_crossentropy', metrics =['accuracy'])
In [246]:
# Set callback functions to early stop training and save the best model so far
callbacks1 = [EarlyStopping(monitor='val_loss', patience=5,min_delta=0,verbose=1),ModelCheckpoint(filepath='best_model2.h5', monitor='val_loss', save_best_only=True)]
In [247]:
# Set callback functions to early stop training and save the best model so far
callbacks2 = [EarlyStopping(monitor='val_loss', patience=10,min_delta=0,verbose=1),ModelCheckpoint(filepath='best_model3.h5', monitor='val_loss', save_best_only=True)]
In [248]:
%%time
# Fit the model
classifier.fit(X_train, y_train, epochs=100, batch_size=10000,validation_data=(X_val, y_val),callbacks=callbacks1)
Train on 2497802 samples, validate on 624451 samples
Epoch 1/100
2210000/2497802 [=========================>....] - ETA: 4:29 - loss: 0.7096 - acc: 0.493 - ETA: 2:31 - loss: 0.7085 - acc: 0.495 - ETA: 1:48 - loss: 0.7072 - acc: 0.495 - ETA: 1:24 - loss: 0.7065 - acc: 0.496 - ETA: 1:10 - loss: 0.7056 - acc: 0.498 - ETA: 1:01 - loss: 0.7054 - acc: 0.498 - ETA: 55s - loss: 0.7053 - acc: 0.498 - ETA: 50s - loss: 0.7053 - acc: 0.49 - ETA: 45s - loss: 0.7050 - acc: 0.49 - ETA: 43s - loss: 0.7048 - acc: 0.49 - ETA: 40s - loss: 0.7043 - acc: 0.50 - ETA: 38s - loss: 0.7036 - acc: 0.50 - ETA: 36s - loss: 0.7029 - acc: 0.50 - ETA: 34s - loss: 0.7025 - acc: 0.50 - ETA: 33s - loss: 0.7023 - acc: 0.50 - ETA: 32s - loss: 0.7022 - acc: 0.50 - ETA: 31s - loss: 0.7019 - acc: 0.50 - ETA: 30s - loss: 0.7016 - acc: 0.50 - ETA: 29s - loss: 0.7014 - acc: 0.50 - ETA: 28s - loss: 0.7012 - acc: 0.50 - ETA: 27s - loss: 0.7010 - acc: 0.50 - ETA: 27s - loss: 0.7008 - acc: 0.50 - ETA: 26s - loss: 0.7005 - acc: 0.50 - ETA: 25s - loss: 0.7001 - acc: 0.50 - ETA: 25s - loss: 0.6998 - 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loss: 0.6826 - acc: 0.556 - ETA: 0s - loss: 0.6826 - acc: 0.556 - ETA: 0s - loss: 0.6826 - acc: 0.557 - ETA: 0s - loss: 0.6825 - acc: 0.557 - ETA: 0s - loss: 0.6825 - acc: 0.557 - 18s 7us/step - loss: 0.6824 - acc: 0.5572 - val_loss: 0.6614 - val_acc: 0.6152
Epoch 2/100
2240000/2497802 [=========================>....] - ETA: 41s - loss: 0.6730 - acc: 0.58 - ETA: 28s - loss: 0.6739 - acc: 0.57 - ETA: 24s - loss: 0.6729 - acc: 0.57 - ETA: 22s - loss: 0.6730 - acc: 0.57 - ETA: 21s - loss: 0.6735 - acc: 0.57 - ETA: 20s - loss: 0.6732 - acc: 0.57 - ETA: 19s - loss: 0.6730 - acc: 0.57 - ETA: 18s - loss: 0.6735 - acc: 0.57 - ETA: 18s - loss: 0.6737 - acc: 0.57 - ETA: 18s - loss: 0.6732 - acc: 0.57 - ETA: 17s - loss: 0.6732 - acc: 0.57 - ETA: 17s - loss: 0.6731 - acc: 0.57 - ETA: 16s - loss: 0.6731 - acc: 0.57 - ETA: 16s - loss: 0.6732 - acc: 0.57 - ETA: 16s - loss: 0.6731 - acc: 0.57 - ETA: 16s - loss: 0.6730 - acc: 0.57 - ETA: 16s - loss: 0.6729 - acc: 0.57 - ETA: 16s - loss: 0.6729 - acc: 0.57 - ETA: 15s - loss: 0.6729 - acc: 0.57 - ETA: 15s - loss: 0.6731 - acc: 0.57 - ETA: 15s - loss: 0.6731 - acc: 0.57 - ETA: 15s - loss: 0.6731 - acc: 0.57 - ETA: 15s - loss: 0.6731 - acc: 0.57 - ETA: 15s - loss: 0.6729 - acc: 0.57 - ETA: 15s - loss: 0.6730 - acc: 0.57 - 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acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.58262497802/2497802 [==============================] - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6712 - acc: 0.582 - ETA: 1s - loss: 0.6711 - acc: 0.582 - ETA: 1s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6711 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.582 - ETA: 0s - loss: 0.6710 - acc: 0.583 - 16s 6us/step - loss: 0.6710 - acc: 0.5830 - val_loss: 0.6562 - val_acc: 0.6194
Epoch 3/100
2150000/2497802 [========================>.....] - ETA: 32s - loss: 0.6699 - acc: 0.58 - ETA: 23s - loss: 0.6708 - acc: 0.58 - ETA: 20s - loss: 0.6719 - acc: 0.58 - ETA: 18s - loss: 0.6712 - acc: 0.58 - ETA: 17s - loss: 0.6708 - acc: 0.58 - ETA: 17s - loss: 0.6702 - acc: 0.58 - ETA: 16s - loss: 0.6706 - acc: 0.58 - ETA: 16s - loss: 0.6704 - acc: 0.58 - ETA: 15s - loss: 0.6707 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6705 - acc: 0.58 - ETA: 15s - loss: 0.6703 - acc: 0.58 - ETA: 14s - loss: 0.6704 - acc: 0.58 - ETA: 14s - loss: 0.6705 - acc: 0.58 - ETA: 14s - loss: 0.6705 - acc: 0.58 - ETA: 14s - loss: 0.6704 - acc: 0.58 - ETA: 14s - loss: 0.6704 - acc: 0.58 - ETA: 14s - loss: 0.6701 - acc: 0.58 - ETA: 14s - loss: 0.6701 - acc: 0.58 - ETA: 14s - loss: 0.6700 - acc: 0.58 - ETA: 14s - loss: 0.6700 - acc: 0.58 - 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Epoch 4/100
2150000/2497802 [========================>.....] - ETA: 37s - loss: 0.6693 - acc: 0.58 - ETA: 25s - loss: 0.6699 - acc: 0.58 - ETA: 21s - loss: 0.6690 - acc: 0.58 - ETA: 20s - loss: 0.6688 - acc: 0.58 - ETA: 19s - loss: 0.6690 - acc: 0.58 - ETA: 19s - loss: 0.6688 - acc: 0.59 - ETA: 19s - loss: 0.6687 - acc: 0.59 - ETA: 18s - loss: 0.6687 - acc: 0.58 - ETA: 18s - loss: 0.6687 - acc: 0.58 - ETA: 18s - loss: 0.6689 - acc: 0.58 - ETA: 17s - loss: 0.6686 - acc: 0.58 - ETA: 17s - loss: 0.6686 - acc: 0.58 - ETA: 17s - loss: 0.6685 - acc: 0.58 - ETA: 17s - loss: 0.6685 - acc: 0.58 - ETA: 17s - loss: 0.6687 - acc: 0.58 - ETA: 17s - loss: 0.6686 - acc: 0.58 - ETA: 16s - loss: 0.6685 - acc: 0.58 - ETA: 16s - loss: 0.6684 - acc: 0.58 - ETA: 16s - loss: 0.6687 - acc: 0.58 - ETA: 16s - loss: 0.6687 - acc: 0.58 - ETA: 16s - loss: 0.6688 - acc: 0.58 - ETA: 16s - loss: 0.6689 - acc: 0.58 - ETA: 16s - loss: 0.6687 - acc: 0.58 - ETA: 16s - loss: 0.6688 - acc: 0.58 - ETA: 15s - loss: 0.6687 - acc: 0.58 - 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acc: 0.588 - ETA: 2s - loss: 0.6685 - acc: 0.588 - ETA: 2s - loss: 0.6684 - acc: 0.588 - ETA: 2s - loss: 0.6684 - acc: 0.588 - ETA: 2s - loss: 0.6684 - acc: 0.58812497802/2497802 [==============================] - ETA: 2s - loss: 0.6684 - acc: 0.588 - ETA: 2s - loss: 0.6684 - acc: 0.588 - ETA: 2s - loss: 0.6684 - acc: 0.588 - ETA: 2s - loss: 0.6684 - acc: 0.588 - ETA: 2s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 1s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - ETA: 0s - loss: 0.6684 - acc: 0.588 - 18s 7us/step - loss: 0.6684 - acc: 0.5885 - val_loss: 0.6545 - val_acc: 0.6218
Epoch 5/100
2150000/2497802 [========================>.....] - ETA: 37s - loss: 0.6758 - acc: 0.57 - ETA: 25s - loss: 0.6707 - acc: 0.58 - ETA: 23s - loss: 0.6695 - acc: 0.58 - ETA: 21s - loss: 0.6693 - acc: 0.58 - ETA: 20s - loss: 0.6692 - acc: 0.58 - ETA: 19s - loss: 0.6691 - acc: 0.58 - ETA: 19s - loss: 0.6692 - acc: 0.58 - ETA: 18s - loss: 0.6689 - acc: 0.58 - ETA: 18s - loss: 0.6691 - acc: 0.58 - ETA: 18s - loss: 0.6692 - acc: 0.58 - ETA: 17s - loss: 0.6694 - acc: 0.58 - ETA: 17s - loss: 0.6689 - acc: 0.58 - ETA: 17s - loss: 0.6688 - acc: 0.58 - ETA: 17s - loss: 0.6687 - acc: 0.58 - ETA: 17s - loss: 0.6687 - acc: 0.58 - ETA: 16s - loss: 0.6687 - acc: 0.58 - ETA: 16s - loss: 0.6689 - acc: 0.58 - ETA: 16s - loss: 0.6689 - acc: 0.58 - ETA: 16s - loss: 0.6690 - acc: 0.58 - ETA: 16s - loss: 0.6691 - acc: 0.58 - ETA: 16s - loss: 0.6690 - acc: 0.58 - ETA: 16s - loss: 0.6690 - acc: 0.58 - ETA: 16s - loss: 0.6689 - acc: 0.58 - ETA: 16s - loss: 0.6691 - acc: 0.58 - ETA: 16s - loss: 0.6690 - acc: 0.58 - 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acc: 0.589 - ETA: 2s - loss: 0.6679 - acc: 0.589 - ETA: 2s - loss: 0.6679 - acc: 0.589 - ETA: 2s - loss: 0.6679 - acc: 0.589 - ETA: 2s - loss: 0.6679 - acc: 0.58912497802/2497802 [==============================] - ETA: 2s - loss: 0.6679 - acc: 0.589 - ETA: 2s - loss: 0.6679 - acc: 0.589 - ETA: 2s - loss: 0.6679 - acc: 0.589 - ETA: 2s - loss: 0.6679 - acc: 0.589 - ETA: 2s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6679 - acc: 0.589 - ETA: 1s - loss: 0.6678 - acc: 0.589 - ETA: 1s - loss: 0.6678 - acc: 0.589 - ETA: 1s - loss: 0.6678 - acc: 0.589 - ETA: 1s - loss: 0.6678 - acc: 0.589 - ETA: 0s - loss: 0.6678 - acc: 0.589 - ETA: 0s - loss: 0.6679 - acc: 0.589 - ETA: 0s - loss: 0.6679 - acc: 0.589 - ETA: 0s - loss: 0.6679 - acc: 0.589 - ETA: 0s - loss: 0.6679 - acc: 0.589 - ETA: 0s - loss: 0.6679 - acc: 0.589 - ETA: 0s - loss: 0.6679 - acc: 0.589 - ETA: 0s - loss: 0.6679 - acc: 0.589 - ETA: 0s - loss: 0.6678 - acc: 0.589 - ETA: 0s - loss: 0.6678 - acc: 0.589 - ETA: 0s - loss: 0.6678 - acc: 0.589 - ETA: 0s - loss: 0.6678 - acc: 0.589 - ETA: 0s - loss: 0.6678 - acc: 0.589 - ETA: 0s - loss: 0.6678 - acc: 0.589 - 18s 7us/step - loss: 0.6678 - acc: 0.5892 - val_loss: 0.6541 - val_acc: 0.6220
Epoch 6/100
2150000/2497802 [========================>.....] - ETA: 36s - loss: 0.6670 - acc: 0.59 - ETA: 25s - loss: 0.6664 - acc: 0.59 - ETA: 21s - loss: 0.6675 - acc: 0.59 - ETA: 20s - loss: 0.6672 - acc: 0.59 - ETA: 19s - loss: 0.6667 - acc: 0.59 - ETA: 19s - loss: 0.6669 - acc: 0.59 - ETA: 19s - loss: 0.6671 - acc: 0.59 - ETA: 18s - loss: 0.6674 - acc: 0.59 - ETA: 18s - loss: 0.6674 - acc: 0.59 - ETA: 18s - loss: 0.6676 - acc: 0.58 - ETA: 18s - loss: 0.6674 - acc: 0.59 - ETA: 18s - loss: 0.6672 - acc: 0.59 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6672 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 16s - loss: 0.6673 - acc: 0.58 - ETA: 16s - loss: 0.6674 - acc: 0.58 - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - 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Epoch 7/100
2150000/2497802 [========================>.....] - ETA: 37s - loss: 0.6685 - acc: 0.58 - ETA: 26s - loss: 0.6674 - acc: 0.59 - ETA: 22s - loss: 0.6675 - acc: 0.59 - ETA: 20s - loss: 0.6686 - acc: 0.58 - ETA: 19s - loss: 0.6684 - acc: 0.58 - ETA: 19s - loss: 0.6684 - acc: 0.58 - ETA: 18s - loss: 0.6683 - acc: 0.58 - ETA: 18s - loss: 0.6684 - acc: 0.58 - ETA: 18s - loss: 0.6686 - acc: 0.58 - ETA: 18s - loss: 0.6684 - acc: 0.58 - ETA: 17s - loss: 0.6684 - acc: 0.58 - ETA: 17s - loss: 0.6684 - acc: 0.58 - ETA: 17s - loss: 0.6684 - acc: 0.58 - ETA: 17s - loss: 0.6684 - acc: 0.58 - ETA: 17s - loss: 0.6682 - acc: 0.58 - ETA: 17s - loss: 0.6682 - acc: 0.58 - ETA: 16s - loss: 0.6680 - acc: 0.58 - ETA: 16s - loss: 0.6679 - acc: 0.58 - ETA: 16s - loss: 0.6678 - acc: 0.59 - ETA: 16s - loss: 0.6677 - acc: 0.59 - ETA: 16s - loss: 0.6676 - acc: 0.59 - ETA: 16s - loss: 0.6676 - acc: 0.59 - ETA: 16s - loss: 0.6675 - acc: 0.59 - ETA: 15s - loss: 0.6675 - acc: 0.59 - ETA: 15s - loss: 0.6676 - acc: 0.58 - 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acc: 0.589 - ETA: 2s - loss: 0.6673 - acc: 0.589 - ETA: 2s - loss: 0.6673 - acc: 0.589 - ETA: 2s - loss: 0.6673 - acc: 0.589 - ETA: 2s - loss: 0.6673 - acc: 0.58952497802/2497802 [==============================] - ETA: 2s - loss: 0.6673 - acc: 0.589 - ETA: 2s - loss: 0.6674 - acc: 0.589 - ETA: 2s - loss: 0.6674 - acc: 0.589 - ETA: 2s - loss: 0.6674 - acc: 0.589 - ETA: 2s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6673 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 1s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - ETA: 0s - loss: 0.6674 - acc: 0.589 - 18s 7us/step - loss: 0.6674 - acc: 0.5895 - val_loss: 0.6537 - val_acc: 0.6230
Epoch 8/100
2150000/2497802 [========================>.....] - ETA: 37s - loss: 0.6683 - acc: 0.58 - ETA: 25s - loss: 0.6680 - acc: 0.58 - ETA: 21s - loss: 0.6668 - acc: 0.58 - ETA: 20s - loss: 0.6669 - acc: 0.58 - ETA: 19s - loss: 0.6661 - acc: 0.59 - ETA: 19s - loss: 0.6667 - acc: 0.58 - ETA: 18s - loss: 0.6669 - acc: 0.58 - ETA: 18s - loss: 0.6669 - acc: 0.58 - ETA: 18s - loss: 0.6674 - acc: 0.58 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 17s - loss: 0.6679 - acc: 0.58 - ETA: 17s - loss: 0.6676 - acc: 0.58 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - ETA: 16s - loss: 0.6674 - acc: 0.58 - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 16s - loss: 0.6675 - acc: 0.58 - 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acc: 0.58 - ETA: 10s - loss: 0.6672 - acc: 0.58 - ETA: 10s - loss: 0.6672 - acc: 0.58 - ETA: 9s - loss: 0.6672 - acc: 0.5897 - ETA: 9s - loss: 0.6672 - acc: 0.589 - ETA: 9s - loss: 0.6672 - acc: 0.589 - ETA: 9s - loss: 0.6672 - acc: 0.589 - ETA: 9s - loss: 0.6672 - acc: 0.589 - ETA: 9s - loss: 0.6672 - acc: 0.589 - ETA: 9s - loss: 0.6672 - acc: 0.589 - ETA: 9s - loss: 0.6672 - acc: 0.589 - ETA: 9s - loss: 0.6672 - acc: 0.589 - ETA: 9s - loss: 0.6672 - acc: 0.589 - ETA: 9s - loss: 0.6672 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - 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acc: 0.589 - ETA: 2s - loss: 0.6672 - acc: 0.589 - ETA: 2s - loss: 0.6672 - acc: 0.589 - ETA: 2s - loss: 0.6672 - acc: 0.589 - ETA: 2s - loss: 0.6672 - acc: 0.58962497802/2497802 [==============================] - ETA: 2s - loss: 0.6672 - acc: 0.589 - ETA: 2s - loss: 0.6672 - acc: 0.589 - ETA: 2s - loss: 0.6672 - acc: 0.589 - ETA: 2s - loss: 0.6672 - acc: 0.589 - ETA: 2s - loss: 0.6672 - acc: 0.589 - ETA: 1s - loss: 0.6672 - acc: 0.589 - ETA: 1s - loss: 0.6672 - acc: 0.589 - ETA: 1s - loss: 0.6672 - acc: 0.589 - ETA: 1s - loss: 0.6672 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6672 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6671 - acc: 0.589 - ETA: 1s - loss: 0.6672 - acc: 0.589 - ETA: 1s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - ETA: 0s - loss: 0.6672 - acc: 0.589 - 18s 7us/step - loss: 0.6672 - acc: 0.5898 - val_loss: 0.6540 - val_acc: 0.6228
Epoch 9/100
2150000/2497802 [========================>.....] - ETA: 33s - loss: 0.6650 - acc: 0.59 - ETA: 24s - loss: 0.6652 - acc: 0.59 - ETA: 21s - loss: 0.6657 - acc: 0.59 - ETA: 19s - loss: 0.6664 - acc: 0.59 - ETA: 19s - loss: 0.6667 - acc: 0.59 - ETA: 18s - loss: 0.6670 - acc: 0.59 - ETA: 18s - loss: 0.6671 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.59 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 17s - loss: 0.6676 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.59 - ETA: 17s - loss: 0.6671 - acc: 0.59 - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 17s - loss: 0.6676 - acc: 0.58 - ETA: 17s - loss: 0.6677 - acc: 0.58 - ETA: 17s - loss: 0.6676 - acc: 0.58 - ETA: 17s - loss: 0.6678 - acc: 0.58 - ETA: 17s - loss: 0.6678 - acc: 0.58 - ETA: 16s - loss: 0.6677 - acc: 0.58 - ETA: 16s - loss: 0.6677 - acc: 0.58 - ETA: 16s - loss: 0.6677 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - ETA: 16s - loss: 0.6676 - acc: 0.58 - 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ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - ETA: 0s - loss: 0.6671 - acc: 0.589 - 18s 7us/step - loss: 0.6671 - acc: 0.5897 - val_loss: 0.6538 - val_acc: 0.6232
Epoch 10/100
2150000/2497802 [========================>.....] - ETA: 37s - loss: 0.6687 - acc: 0.58 - ETA: 26s - loss: 0.6671 - acc: 0.58 - ETA: 22s - loss: 0.6678 - acc: 0.58 - ETA: 21s - loss: 0.6668 - acc: 0.58 - ETA: 20s - loss: 0.6670 - acc: 0.58 - ETA: 19s - loss: 0.6673 - acc: 0.58 - ETA: 19s - loss: 0.6670 - acc: 0.58 - ETA: 18s - loss: 0.6668 - acc: 0.58 - ETA: 18s - loss: 0.6670 - acc: 0.58 - ETA: 18s - loss: 0.6672 - acc: 0.58 - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 17s - loss: 0.6676 - acc: 0.58 - ETA: 17s - loss: 0.6675 - acc: 0.58 - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 16s - loss: 0.6674 - acc: 0.58 - ETA: 16s - loss: 0.6671 - acc: 0.58 - ETA: 16s - loss: 0.6671 - acc: 0.58 - ETA: 16s - loss: 0.6671 - acc: 0.58 - ETA: 16s - loss: 0.6670 - acc: 0.59 - ETA: 16s - loss: 0.6668 - acc: 0.59 - ETA: 16s - loss: 0.6668 - acc: 0.59 - ETA: 16s - loss: 0.6667 - acc: 0.59 - ETA: 16s - loss: 0.6667 - acc: 0.59 - ETA: 16s - loss: 0.6668 - acc: 0.59 - ETA: 15s - loss: 0.6668 - acc: 0.59 - 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acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.58992497802/2497802 [==============================] - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - 18s 7us/step - loss: 0.6670 - acc: 0.5896 - val_loss: 0.6550 - val_acc: 0.6232
Epoch 11/100
2150000/2497802 [========================>.....] - ETA: 36s - loss: 0.6695 - acc: 0.58 - ETA: 26s - loss: 0.6671 - acc: 0.58 - ETA: 22s - loss: 0.6668 - acc: 0.58 - ETA: 21s - loss: 0.6673 - acc: 0.58 - ETA: 19s - loss: 0.6675 - acc: 0.58 - ETA: 19s - loss: 0.6673 - acc: 0.58 - ETA: 18s - loss: 0.6676 - acc: 0.58 - ETA: 18s - loss: 0.6677 - acc: 0.58 - ETA: 18s - loss: 0.6675 - acc: 0.59 - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 17s - loss: 0.6671 - acc: 0.58 - ETA: 17s - loss: 0.6670 - acc: 0.58 - ETA: 17s - loss: 0.6671 - acc: 0.58 - ETA: 16s - loss: 0.6672 - acc: 0.58 - ETA: 16s - loss: 0.6672 - acc: 0.58 - ETA: 16s - loss: 0.6671 - acc: 0.59 - ETA: 16s - loss: 0.6671 - acc: 0.59 - ETA: 16s - loss: 0.6671 - acc: 0.59 - ETA: 16s - loss: 0.6671 - acc: 0.59 - ETA: 16s - loss: 0.6672 - acc: 0.59 - ETA: 16s - loss: 0.6671 - acc: 0.59 - ETA: 15s - loss: 0.6670 - acc: 0.59 - ETA: 15s - loss: 0.6669 - acc: 0.58 - ETA: 15s - loss: 0.6670 - acc: 0.59 - ETA: 15s - loss: 0.6670 - acc: 0.59 - 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acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - 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acc: 0.590 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6669 - acc: 0.590 - ETA: 4s - loss: 0.6669 - acc: 0.590 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - 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acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.59002497802/2497802 [==============================] - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6668 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.590 - 18s 7us/step - loss: 0.6669 - acc: 0.5900 - val_loss: 0.6546 - val_acc: 0.6231
Epoch 12/100
2150000/2497802 [========================>.....] - ETA: 40s - loss: 0.6640 - acc: 0.59 - ETA: 28s - loss: 0.6669 - acc: 0.59 - ETA: 23s - loss: 0.6669 - acc: 0.58 - ETA: 21s - loss: 0.6668 - acc: 0.58 - ETA: 20s - loss: 0.6663 - acc: 0.59 - ETA: 19s - loss: 0.6670 - acc: 0.58 - ETA: 19s - loss: 0.6668 - acc: 0.58 - ETA: 18s - loss: 0.6666 - acc: 0.58 - ETA: 18s - loss: 0.6666 - acc: 0.58 - ETA: 18s - loss: 0.6667 - acc: 0.58 - ETA: 18s - loss: 0.6667 - acc: 0.58 - ETA: 18s - loss: 0.6669 - acc: 0.58 - ETA: 17s - loss: 0.6670 - acc: 0.58 - ETA: 17s - loss: 0.6670 - acc: 0.58 - ETA: 17s - loss: 0.6671 - acc: 0.58 - ETA: 17s - loss: 0.6673 - acc: 0.58 - ETA: 17s - loss: 0.6672 - acc: 0.58 - ETA: 16s - loss: 0.6673 - acc: 0.58 - ETA: 16s - loss: 0.6672 - acc: 0.58 - ETA: 16s - loss: 0.6670 - acc: 0.58 - ETA: 16s - loss: 0.6669 - acc: 0.58 - ETA: 16s - loss: 0.6669 - acc: 0.58 - ETA: 16s - loss: 0.6672 - acc: 0.58 - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 16s - loss: 0.6674 - acc: 0.58 - 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acc: 0.5894 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6670 - acc: 0.589 - ETA: 9s - loss: 0.6670 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6672 - acc: 0.589 - ETA: 8s - loss: 0.6672 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - 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acc: 0.589 - ETA: 6s - loss: 0.6670 - acc: 0.589 - ETA: 6s - loss: 0.6671 - acc: 0.589 - ETA: 6s - loss: 0.6671 - acc: 0.589 - ETA: 6s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6670 - acc: 0.589 - ETA: 5s - loss: 0.6671 - acc: 0.589 - ETA: 5s - loss: 0.6670 - acc: 0.589 - ETA: 5s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6671 - acc: 0.589 - ETA: 4s - loss: 0.6671 - acc: 0.589 - ETA: 4s - loss: 0.6671 - acc: 0.589 - ETA: 4s - loss: 0.6671 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6671 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 3s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.58952497802/2497802 [==============================] - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6670 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - 18s 7us/step - loss: 0.6669 - acc: 0.5897 - val_loss: 0.6538 - val_acc: 0.6234
Epoch 00012: early stopping
Wall time: 3min 33s
Out[248]:
<keras.callbacks.History at 0x26879bcb3c8>
In [249]:
%%time
# Fit the model
#classifier.fit(X_train, y_train, epochs=200, batch_size=500,validation_data=(X_val, y_val),callbacks=callbacks1)
Wall time: 0 ns
In [250]:
%%time
# Fit the model
#classifier.fit(X_train, y_train, epochs=1000, batch_size=5000,validation_data=(X_val, y_val),callbacks=callbacks1)
Wall time: 0 ns
In [251]:
%%time
# Fit the model
classifier.fit(X_train, y_train, epochs=1000, batch_size=50000,validation_data=(X_val, y_val),callbacks=callbacks2)
Train on 2497802 samples, validate on 624451 samples
Epoch 1/1000
2497802/2497802 [==============================] - ETA: 17s - loss: 0.6679 - acc: 0.58 - ETA: 15s - loss: 0.6672 - acc: 0.58 - ETA: 14s - loss: 0.6670 - acc: 0.59 - ETA: 14s - loss: 0.6672 - acc: 0.59 - ETA: 13s - loss: 0.6672 - acc: 0.59 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 12s - loss: 0.6674 - acc: 0.58 - ETA: 12s - loss: 0.6673 - acc: 0.58 - ETA: 12s - loss: 0.6672 - acc: 0.58 - ETA: 11s - loss: 0.6673 - acc: 0.58 - ETA: 11s - loss: 0.6673 - acc: 0.58 - ETA: 11s - loss: 0.6673 - acc: 0.58 - ETA: 11s - loss: 0.6673 - acc: 0.58 - ETA: 10s - loss: 0.6672 - acc: 0.58 - ETA: 10s - loss: 0.6672 - acc: 0.58 - ETA: 10s - loss: 0.6672 - acc: 0.58 - ETA: 9s - loss: 0.6671 - acc: 0.5899 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 9s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6671 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 7s - loss: 0.6669 - acc: 0.589 - ETA: 7s - loss: 0.6670 - acc: 0.589 - ETA: 7s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6670 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6668 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6668 - acc: 0.589 - 16s 6us/step - loss: 0.6668 - acc: 0.5900 - val_loss: 0.6539 - val_acc: 0.6235
Epoch 2/1000
2497802/2497802 [==============================] - ETA: 19s - loss: 0.6661 - acc: 0.59 - ETA: 16s - loss: 0.6662 - acc: 0.59 - ETA: 15s - loss: 0.6661 - acc: 0.59 - ETA: 14s - loss: 0.6657 - acc: 0.59 - ETA: 14s - loss: 0.6659 - acc: 0.59 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6660 - acc: 0.59 - ETA: 11s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5904 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 8s - loss: 0.6669 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 8s - loss: 0.6669 - acc: 0.590 - ETA: 7s - loss: 0.6669 - acc: 0.590 - ETA: 7s - loss: 0.6669 - acc: 0.590 - ETA: 7s - loss: 0.6669 - acc: 0.590 - ETA: 6s - loss: 0.6669 - acc: 0.590 - ETA: 6s - loss: 0.6669 - acc: 0.590 - ETA: 6s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.590 - ETA: 4s - loss: 0.6669 - acc: 0.590 - ETA: 4s - loss: 0.6669 - acc: 0.590 - ETA: 4s - loss: 0.6669 - acc: 0.590 - ETA: 3s - loss: 0.6669 - acc: 0.590 - ETA: 3s - loss: 0.6669 - acc: 0.590 - ETA: 3s - loss: 0.6670 - acc: 0.590 - ETA: 2s - loss: 0.6670 - acc: 0.590 - ETA: 2s - loss: 0.6669 - acc: 0.590 - ETA: 2s - loss: 0.6669 - acc: 0.590 - ETA: 2s - loss: 0.6669 - acc: 0.590 - ETA: 1s - loss: 0.6669 - acc: 0.590 - ETA: 1s - loss: 0.6669 - acc: 0.590 - ETA: 1s - loss: 0.6670 - acc: 0.590 - ETA: 0s - loss: 0.6670 - acc: 0.590 - ETA: 0s - loss: 0.6670 - acc: 0.590 - ETA: 0s - loss: 0.6670 - acc: 0.590 - 16s 6us/step - loss: 0.6669 - acc: 0.5900 - val_loss: 0.6542 - val_acc: 0.6232
Epoch 3/1000
2497802/2497802 [==============================] - ETA: 19s - loss: 0.6676 - acc: 0.58 - ETA: 15s - loss: 0.6674 - acc: 0.58 - ETA: 14s - loss: 0.6676 - acc: 0.58 - ETA: 14s - loss: 0.6674 - acc: 0.58 - ETA: 13s - loss: 0.6674 - acc: 0.58 - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 13s - loss: 0.6670 - acc: 0.58 - ETA: 12s - loss: 0.6670 - acc: 0.58 - ETA: 12s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6672 - acc: 0.58 - ETA: 11s - loss: 0.6672 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 9s - loss: 0.6670 - acc: 0.5897 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 15s 6us/step - loss: 0.6667 - acc: 0.5900 - val_loss: 0.6541 - val_acc: 0.6235
Epoch 4/1000
2497802/2497802 [==============================] - ETA: 18s - loss: 0.6671 - acc: 0.58 - ETA: 15s - loss: 0.6668 - acc: 0.58 - ETA: 15s - loss: 0.6667 - acc: 0.58 - ETA: 14s - loss: 0.6668 - acc: 0.58 - ETA: 14s - loss: 0.6667 - acc: 0.58 - ETA: 13s - loss: 0.6670 - acc: 0.58 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5903 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 16s 6us/step - loss: 0.6667 - acc: 0.5903 - val_loss: 0.6539 - val_acc: 0.6233
Epoch 5/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6664 - acc: 0.59 - ETA: 16s - loss: 0.6670 - acc: 0.58 - ETA: 15s - loss: 0.6671 - acc: 0.58 - ETA: 14s - loss: 0.6670 - acc: 0.58 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 13s - loss: 0.6666 - acc: 0.59 - ETA: 13s - loss: 0.6666 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6665 - acc: 0.5908 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 16s 6us/step - loss: 0.6665 - acc: 0.5906 - val_loss: 0.6539 - val_acc: 0.6236
Epoch 6/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6657 - acc: 0.58 - ETA: 14s - loss: 0.6666 - acc: 0.58 - ETA: 14s - loss: 0.6663 - acc: 0.59 - ETA: 15s - loss: 0.6665 - acc: 0.59 - ETA: 14s - loss: 0.6666 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 12s - loss: 0.6667 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6669 - acc: 0.59 - ETA: 9s - loss: 0.6669 - acc: 0.5901 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 6s - loss: 0.6669 - acc: 0.590 - ETA: 6s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.590 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 4s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 3s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 2s - loss: 0.6670 - acc: 0.589 - ETA: 2s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.589 - ETA: 1s - loss: 0.6669 - acc: 0.590 - ETA: 1s - loss: 0.6669 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.590 - ETA: 0s - loss: 0.6669 - acc: 0.589 - ETA: 0s - loss: 0.6669 - acc: 0.589 - 15s 6us/step - loss: 0.6669 - acc: 0.5899 - val_loss: 0.6541 - val_acc: 0.6236
Epoch 7/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6660 - acc: 0.58 - ETA: 14s - loss: 0.6661 - acc: 0.58 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6654 - acc: 0.59 - ETA: 13s - loss: 0.6659 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5908 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 6s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 5s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 15s 6us/step - loss: 0.6667 - acc: 0.5902 - val_loss: 0.6537 - val_acc: 0.6237
Epoch 8/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6645 - acc: 0.59 - ETA: 14s - loss: 0.6653 - acc: 0.59 - ETA: 14s - loss: 0.6661 - acc: 0.58 - ETA: 15s - loss: 0.6662 - acc: 0.58 - ETA: 14s - loss: 0.6662 - acc: 0.59 - ETA: 14s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 9s - loss: 0.6664 - acc: 0.5906 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 16s 6us/step - loss: 0.6666 - acc: 0.5903 - val_loss: 0.6543 - val_acc: 0.6236
Epoch 9/1000
2497802/2497802 [==============================] - ETA: 21s - loss: 0.6661 - acc: 0.58 - ETA: 18s - loss: 0.6671 - acc: 0.58 - ETA: 16s - loss: 0.6666 - acc: 0.59 - ETA: 15s - loss: 0.6668 - acc: 0.59 - ETA: 15s - loss: 0.6668 - acc: 0.58 - ETA: 14s - loss: 0.6669 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 12s - loss: 0.6673 - acc: 0.58 - ETA: 12s - loss: 0.6673 - acc: 0.58 - ETA: 11s - loss: 0.6673 - acc: 0.58 - ETA: 11s - loss: 0.6672 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 10s - loss: 0.6669 - acc: 0.58 - ETA: 10s - loss: 0.6669 - acc: 0.58 - ETA: 9s - loss: 0.6667 - acc: 0.5895 - ETA: 9s - loss: 0.6667 - acc: 0.589 - ETA: 9s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6666 - acc: 0.589 - ETA: 5s - loss: 0.6666 - acc: 0.589 - ETA: 5s - loss: 0.6666 - acc: 0.589 - ETA: 4s - loss: 0.6666 - acc: 0.589 - ETA: 4s - loss: 0.6666 - acc: 0.589 - ETA: 4s - loss: 0.6666 - acc: 0.589 - ETA: 3s - loss: 0.6666 - acc: 0.589 - ETA: 3s - loss: 0.6665 - acc: 0.589 - ETA: 3s - loss: 0.6665 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - 15s 6us/step - loss: 0.6666 - acc: 0.5900 - val_loss: 0.6541 - val_acc: 0.6238
Epoch 10/1000
2497802/2497802 [==============================] - ETA: 17s - loss: 0.6674 - acc: 0.58 - ETA: 15s - loss: 0.6676 - acc: 0.58 - ETA: 14s - loss: 0.6668 - acc: 0.58 - ETA: 14s - loss: 0.6666 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6670 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6670 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6670 - acc: 0.58 - ETA: 11s - loss: 0.6668 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 9s - loss: 0.6667 - acc: 0.5898 - ETA: 9s - loss: 0.6666 - acc: 0.589 - ETA: 9s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 7s - loss: 0.6668 - acc: 0.589 - ETA: 7s - loss: 0.6668 - acc: 0.589 - ETA: 7s - loss: 0.6668 - acc: 0.589 - ETA: 6s - loss: 0.6668 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 15s 6us/step - loss: 0.6667 - acc: 0.5902 - val_loss: 0.6537 - val_acc: 0.6237
Epoch 11/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6669 - acc: 0.59 - ETA: 14s - loss: 0.6669 - acc: 0.59 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.58 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.58 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6665 - acc: 0.5906 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5903 - val_loss: 0.6538 - val_acc: 0.6238
Epoch 12/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6672 - acc: 0.58 - ETA: 14s - loss: 0.6666 - acc: 0.58 - ETA: 13s - loss: 0.6667 - acc: 0.58 - ETA: 13s - loss: 0.6666 - acc: 0.58 - ETA: 13s - loss: 0.6663 - acc: 0.58 - ETA: 12s - loss: 0.6664 - acc: 0.58 - ETA: 12s - loss: 0.6667 - acc: 0.58 - ETA: 12s - loss: 0.6667 - acc: 0.58 - ETA: 11s - loss: 0.6667 - acc: 0.58 - ETA: 11s - loss: 0.6665 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6669 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 9s - loss: 0.6670 - acc: 0.5892 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 8s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6670 - acc: 0.589 - ETA: 7s - loss: 0.6670 - acc: 0.589 - ETA: 7s - loss: 0.6670 - acc: 0.589 - ETA: 7s - loss: 0.6670 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 6s - loss: 0.6670 - acc: 0.589 - ETA: 6s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6669 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.589 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 3s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6668 - acc: 0.589 - ETA: 2s - loss: 0.6668 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.589 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5900 - val_loss: 0.6539 - val_acc: 0.6238
Epoch 13/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6654 - acc: 0.59 - ETA: 14s - loss: 0.6660 - acc: 0.59 - ETA: 14s - loss: 0.6669 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6666 - acc: 0.58 - ETA: 12s - loss: 0.6665 - acc: 0.58 - ETA: 11s - loss: 0.6665 - acc: 0.58 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5901 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5903 - val_loss: 0.6542 - val_acc: 0.6239
Epoch 14/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6675 - acc: 0.58 - ETA: 14s - loss: 0.6673 - acc: 0.58 - ETA: 14s - loss: 0.6666 - acc: 0.58 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5909 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5904 - val_loss: 0.6543 - val_acc: 0.6238
Epoch 15/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6670 - acc: 0.59 - ETA: 14s - loss: 0.6672 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.59 - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 13s - loss: 0.6671 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6670 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6670 - acc: 0.58 - ETA: 10s - loss: 0.6669 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6669 - acc: 0.58 - ETA: 9s - loss: 0.6668 - acc: 0.5899 - ETA: 9s - loss: 0.6668 - acc: 0.589 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5902 - val_loss: 0.6540 - val_acc: 0.6239
Epoch 16/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6657 - acc: 0.59 - ETA: 14s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6664 - acc: 0.58 - ETA: 13s - loss: 0.6667 - acc: 0.58 - ETA: 13s - loss: 0.6664 - acc: 0.58 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.58 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.58 - ETA: 11s - loss: 0.6666 - acc: 0.58 - ETA: 11s - loss: 0.6666 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6666 - acc: 0.58 - ETA: 10s - loss: 0.6666 - acc: 0.58 - ETA: 9s - loss: 0.6667 - acc: 0.5898 - ETA: 9s - loss: 0.6667 - acc: 0.589 - ETA: 9s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5906 - val_loss: 0.6535 - val_acc: 0.6238
Epoch 17/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6666 - acc: 0.59 - ETA: 14s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6658 - acc: 0.59 - ETA: 12s - loss: 0.6657 - acc: 0.59 - ETA: 11s - loss: 0.6658 - acc: 0.59 - ETA: 11s - loss: 0.6660 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 9s - loss: 0.6662 - acc: 0.5904 - ETA: 9s - loss: 0.6662 - acc: 0.590 - ETA: 9s - loss: 0.6662 - acc: 0.590 - ETA: 9s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.589 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 14s 6us/step - loss: 0.6666 - acc: 0.5902 - val_loss: 0.6539 - val_acc: 0.6239
Epoch 18/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6674 - acc: 0.59 - ETA: 14s - loss: 0.6665 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 11s - loss: 0.6668 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 10s - loss: 0.6670 - acc: 0.59 - ETA: 10s - loss: 0.6670 - acc: 0.59 - ETA: 10s - loss: 0.6669 - acc: 0.59 - ETA: 9s - loss: 0.6668 - acc: 0.5904 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 15s 6us/step - loss: 0.6664 - acc: 0.5907 - val_loss: 0.6539 - val_acc: 0.6242
Epoch 19/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6657 - acc: 0.59 - ETA: 15s - loss: 0.6664 - acc: 0.59 - ETA: 14s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5907 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6667 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5903 - val_loss: 0.6541 - val_acc: 0.6241
Epoch 20/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.59 - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6661 - acc: 0.59 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5911 - ETA: 9s - loss: 0.6663 - acc: 0.591 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.589 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5903 - val_loss: 0.6538 - val_acc: 0.6240
Epoch 21/1000
2497802/2497802 [==============================] - ETA: 19s - loss: 0.6668 - acc: 0.58 - ETA: 16s - loss: 0.6668 - acc: 0.58 - ETA: 15s - loss: 0.6672 - acc: 0.58 - ETA: 15s - loss: 0.6670 - acc: 0.58 - ETA: 15s - loss: 0.6672 - acc: 0.58 - ETA: 15s - loss: 0.6670 - acc: 0.58 - ETA: 15s - loss: 0.6671 - acc: 0.58 - ETA: 14s - loss: 0.6670 - acc: 0.58 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6664 - acc: 0.5904 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.589 - ETA: 6s - loss: 0.6668 - acc: 0.589 - ETA: 6s - loss: 0.6667 - acc: 0.589 - ETA: 6s - loss: 0.6668 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6668 - acc: 0.589 - ETA: 4s - loss: 0.6667 - acc: 0.589 - ETA: 4s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 16s 6us/step - loss: 0.6666 - acc: 0.5902 - val_loss: 0.6535 - val_acc: 0.6239
Epoch 22/1000
2497802/2497802 [==============================] - ETA: 19s - loss: 0.6686 - acc: 0.58 - ETA: 16s - loss: 0.6673 - acc: 0.59 - ETA: 15s - loss: 0.6667 - acc: 0.59 - ETA: 14s - loss: 0.6666 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6666 - acc: 0.59 - ETA: 13s - loss: 0.6666 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 10s - loss: 0.6669 - acc: 0.59 - ETA: 10s - loss: 0.6669 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 9s - loss: 0.6667 - acc: 0.5903 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6668 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 16s 6us/step - loss: 0.6665 - acc: 0.5902 - val_loss: 0.6540 - val_acc: 0.6240
Epoch 23/1000
2497802/2497802 [==============================] - ETA: 20s - loss: 0.6680 - acc: 0.59 - ETA: 16s - loss: 0.6674 - acc: 0.59 - ETA: 14s - loss: 0.6672 - acc: 0.59 - ETA: 13s - loss: 0.6670 - acc: 0.59 - ETA: 13s - loss: 0.6669 - acc: 0.59 - ETA: 13s - loss: 0.6669 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6667 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 9s - loss: 0.6669 - acc: 0.5901 - ETA: 9s - loss: 0.6669 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 9s - loss: 0.6668 - acc: 0.590 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6668 - acc: 0.589 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.589 - ETA: 5s - loss: 0.6666 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 5s - loss: 0.6667 - acc: 0.589 - ETA: 4s - loss: 0.6667 - acc: 0.589 - ETA: 4s - loss: 0.6667 - acc: 0.589 - ETA: 4s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 3s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6667 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6667 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - 15s 6us/step - loss: 0.6666 - acc: 0.5899 - val_loss: 0.6541 - val_acc: 0.6241
Epoch 24/1000
2497802/2497802 [==============================] - ETA: 18s - loss: 0.6661 - acc: 0.59 - ETA: 16s - loss: 0.6669 - acc: 0.58 - ETA: 15s - loss: 0.6667 - acc: 0.58 - ETA: 14s - loss: 0.6668 - acc: 0.58 - ETA: 14s - loss: 0.6669 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.59 - ETA: 13s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5908 - ETA: 9s - loss: 0.6663 - acc: 0.590 - ETA: 9s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5905 - val_loss: 0.6538 - val_acc: 0.6242
Epoch 25/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6677 - acc: 0.58 - ETA: 14s - loss: 0.6674 - acc: 0.58 - ETA: 14s - loss: 0.6673 - acc: 0.58 - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 12s - loss: 0.6670 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 11s - loss: 0.6670 - acc: 0.58 - ETA: 11s - loss: 0.6669 - acc: 0.59 - ETA: 11s - loss: 0.6669 - acc: 0.58 - ETA: 11s - loss: 0.6668 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 10s - loss: 0.6668 - acc: 0.58 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 9s - loss: 0.6668 - acc: 0.5896 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 9s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 5s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5907 - val_loss: 0.6539 - val_acc: 0.6242
Epoch 26/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6686 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6665 - acc: 0.5905 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 15s 6us/step - loss: 0.6666 - acc: 0.5901 - val_loss: 0.6540 - val_acc: 0.6240
Epoch 27/1000
2497802/2497802 [==============================] - ETA: 18s - loss: 0.6674 - acc: 0.59 - ETA: 16s - loss: 0.6672 - acc: 0.58 - ETA: 15s - loss: 0.6670 - acc: 0.58 - ETA: 15s - loss: 0.6668 - acc: 0.59 - ETA: 15s - loss: 0.6668 - acc: 0.59 - ETA: 14s - loss: 0.6669 - acc: 0.59 - ETA: 14s - loss: 0.6671 - acc: 0.59 - ETA: 13s - loss: 0.6671 - acc: 0.59 - ETA: 13s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6668 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 9s - loss: 0.6667 - acc: 0.5903 - ETA: 9s - loss: 0.6667 - acc: 0.590 - ETA: 9s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 17s 7us/step - loss: 0.6666 - acc: 0.5901 - val_loss: 0.6544 - val_acc: 0.6240
Epoch 28/1000
2497802/2497802 [==============================] - ETA: 20s - loss: 0.6648 - acc: 0.59 - ETA: 18s - loss: 0.6662 - acc: 0.59 - ETA: 17s - loss: 0.6665 - acc: 0.59 - ETA: 16s - loss: 0.6659 - acc: 0.59 - ETA: 15s - loss: 0.6660 - acc: 0.59 - ETA: 15s - loss: 0.6661 - acc: 0.59 - ETA: 15s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6664 - acc: 0.59 - ETA: 14s - loss: 0.6663 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.58 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6662 - acc: 0.5902 - ETA: 9s - loss: 0.6662 - acc: 0.590 - ETA: 8s - loss: 0.6662 - acc: 0.590 - ETA: 8s - loss: 0.6662 - acc: 0.590 - ETA: 8s - loss: 0.6662 - acc: 0.590 - ETA: 7s - loss: 0.6662 - acc: 0.590 - ETA: 7s - loss: 0.6662 - acc: 0.590 - ETA: 7s - loss: 0.6662 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 6s - loss: 0.6662 - acc: 0.590 - ETA: 6s - loss: 0.6662 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6663 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 17s 7us/step - loss: 0.6665 - acc: 0.5903 - val_loss: 0.6545 - val_acc: 0.6240
Epoch 29/1000
2497802/2497802 [==============================] - ETA: 20s - loss: 0.6668 - acc: 0.59 - ETA: 17s - loss: 0.6668 - acc: 0.59 - ETA: 16s - loss: 0.6663 - acc: 0.59 - ETA: 16s - loss: 0.6662 - acc: 0.59 - ETA: 15s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6664 - acc: 0.59 - ETA: 14s - loss: 0.6664 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6667 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 9s - loss: 0.6665 - acc: 0.5905 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 9s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 8s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 16s 6us/step - loss: 0.6665 - acc: 0.5906 - val_loss: 0.6543 - val_acc: 0.6242
Epoch 30/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6671 - acc: 0.58 - ETA: 14s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6668 - acc: 0.58 - ETA: 13s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6666 - acc: 0.58 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 12s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6661 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.58 - ETA: 10s - loss: 0.6665 - acc: 0.58 - ETA: 10s - loss: 0.6665 - acc: 0.58 - ETA: 10s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6664 - acc: 0.5901 - ETA: 9s - loss: 0.6663 - acc: 0.590 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6663 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 2s - loss: 0.6667 - acc: 0.589 - ETA: 2s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.589 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - ETA: 0s - loss: 0.6666 - acc: 0.589 - 15s 6us/step - loss: 0.6666 - acc: 0.5899 - val_loss: 0.6539 - val_acc: 0.6242
Epoch 31/1000
2497802/2497802 [==============================] - ETA: 17s - loss: 0.6654 - acc: 0.59 - ETA: 15s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6674 - acc: 0.58 - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 13s - loss: 0.6672 - acc: 0.58 - ETA: 13s - loss: 0.6671 - acc: 0.58 - ETA: 12s - loss: 0.6669 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.58 - ETA: 12s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 11s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 10s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6671 - acc: 0.58 - ETA: 10s - loss: 0.6670 - acc: 0.58 - ETA: 9s - loss: 0.6669 - acc: 0.5894 - ETA: 9s - loss: 0.6669 - acc: 0.589 - ETA: 9s - loss: 0.6668 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.589 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 16s 6us/step - loss: 0.6665 - acc: 0.5904 - val_loss: 0.6543 - val_acc: 0.6244
Epoch 00031: early stopping
Wall time: 7min 54s
Out[251]:
<keras.callbacks.History at 0x2689253c2e8>
In [253]:
%%time
# Fit the model
classifier.fit(X_train, y_train, epochs=1000, batch_size=200000,validation_data=(X_val, y_val),callbacks=callbacks2)
Train on 2497802 samples, validate on 624451 samples
Epoch 1/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6667 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6664 - acc: 0.5908 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6663 - acc: 0.590 - ETA: 0s - loss: 0.6663 - acc: 0.590 - 17s 7us/step - loss: 0.6664 - acc: 0.5905 - val_loss: 0.6541 - val_acc: 0.6243
Epoch 2/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6663 - acc: 0.59 - ETA: 14s - loss: 0.6667 - acc: 0.59 - ETA: 12s - loss: 0.6668 - acc: 0.58 - ETA: 11s - loss: 0.6666 - acc: 0.58 - ETA: 9s - loss: 0.6665 - acc: 0.5899 - ETA: 8s - loss: 0.6665 - acc: 0.589 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 18s 7us/step - loss: 0.6664 - acc: 0.5902 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 3/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.59 - ETA: 9s - loss: 0.6666 - acc: 0.5902 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6666 - acc: 0.590 - 16s 6us/step - loss: 0.6666 - acc: 0.5902 - val_loss: 0.6542 - val_acc: 0.6244
Epoch 4/1000
2497802/2497802 [==============================] - ETA: 14s - loss: 0.6656 - acc: 0.59 - ETA: 12s - loss: 0.6658 - acc: 0.59 - ETA: 11s - loss: 0.6661 - acc: 0.59 - ETA: 10s - loss: 0.6660 - acc: 0.59 - ETA: 8s - loss: 0.6661 - acc: 0.5910 - ETA: 7s - loss: 0.6662 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6662 - acc: 0.590 - ETA: 0s - loss: 0.6662 - acc: 0.590 - 15s 6us/step - loss: 0.6662 - acc: 0.5907 - val_loss: 0.6540 - val_acc: 0.6244
Epoch 5/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 12s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6661 - acc: 0.59 - ETA: 8s - loss: 0.6665 - acc: 0.5907 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 4s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 16s 6us/step - loss: 0.6666 - acc: 0.5904 - val_loss: 0.6544 - val_acc: 0.6243
Epoch 6/1000
2497802/2497802 [==============================] - ETA: 14s - loss: 0.6657 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.58 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6667 - acc: 0.58 - ETA: 8s - loss: 0.6667 - acc: 0.5899 - ETA: 7s - loss: 0.6667 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 15s 6us/step - loss: 0.6664 - acc: 0.5902 - val_loss: 0.6540 - val_acc: 0.6244
Epoch 7/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6660 - acc: 0.59 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 11s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5907 - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6662 - acc: 0.591 - ETA: 0s - loss: 0.6662 - acc: 0.591 - 16s 6us/step - loss: 0.6662 - acc: 0.5911 - val_loss: 0.6540 - val_acc: 0.6244
Epoch 8/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6662 - acc: 0.58 - ETA: 12s - loss: 0.6666 - acc: 0.58 - ETA: 11s - loss: 0.6665 - acc: 0.58 - ETA: 9s - loss: 0.6668 - acc: 0.5895 - ETA: 8s - loss: 0.6667 - acc: 0.589 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6666 - acc: 0.590 - ETA: 5s - loss: 0.6666 - acc: 0.590 - ETA: 3s - loss: 0.6667 - acc: 0.590 - ETA: 2s - loss: 0.6666 - acc: 0.590 - ETA: 1s - loss: 0.6666 - acc: 0.590 - ETA: 0s - loss: 0.6667 - acc: 0.590 - 15s 6us/step - loss: 0.6667 - acc: 0.5901 - val_loss: 0.6544 - val_acc: 0.6243
Epoch 9/1000
2497802/2497802 [==============================] - ETA: 14s - loss: 0.6662 - acc: 0.59 - ETA: 12s - loss: 0.6664 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5907 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6662 - acc: 0.590 - ETA: 2s - loss: 0.6662 - acc: 0.590 - ETA: 1s - loss: 0.6661 - acc: 0.591 - ETA: 0s - loss: 0.6661 - acc: 0.591 - 15s 6us/step - loss: 0.6661 - acc: 0.5910 - val_loss: 0.6539 - val_acc: 0.6243
Epoch 10/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6661 - acc: 0.58 - ETA: 12s - loss: 0.6660 - acc: 0.59 - ETA: 11s - loss: 0.6661 - acc: 0.59 - ETA: 10s - loss: 0.6659 - acc: 0.59 - ETA: 9s - loss: 0.6659 - acc: 0.5909 - ETA: 8s - loss: 0.6661 - acc: 0.590 - ETA: 6s - loss: 0.6662 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6662 - acc: 0.590 - ETA: 3s - loss: 0.6662 - acc: 0.590 - ETA: 1s - loss: 0.6662 - acc: 0.590 - ETA: 0s - loss: 0.6662 - acc: 0.590 - 16s 6us/step - loss: 0.6663 - acc: 0.5906 - val_loss: 0.6541 - val_acc: 0.6243
Epoch 11/1000
2497802/2497802 [==============================] - ETA: 14s - loss: 0.6658 - acc: 0.59 - ETA: 13s - loss: 0.6662 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5907 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6665 - acc: 0.590 - ETA: 1s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6665 - acc: 0.590 - 17s 7us/step - loss: 0.6665 - acc: 0.5902 - val_loss: 0.6543 - val_acc: 0.6243
Epoch 12/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6661 - acc: 0.59 - ETA: 14s - loss: 0.6661 - acc: 0.59 - ETA: 13s - loss: 0.6663 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 9s - loss: 0.6662 - acc: 0.5914 - ETA: 7s - loss: 0.6662 - acc: 0.591 - ETA: 6s - loss: 0.6662 - acc: 0.591 - ETA: 4s - loss: 0.6663 - acc: 0.591 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 18s 7us/step - loss: 0.6664 - acc: 0.5907 - val_loss: 0.6542 - val_acc: 0.6242
Epoch 13/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6664 - acc: 0.59 - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.58 - ETA: 10s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5906 - ETA: 7s - loss: 0.6663 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 16s 7us/step - loss: 0.6664 - acc: 0.5904 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 14/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6665 - acc: 0.59 - ETA: 14s - loss: 0.6666 - acc: 0.58 - ETA: 12s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5906 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6663 - acc: 0.590 - ETA: 0s - loss: 0.6663 - acc: 0.590 - 17s 7us/step - loss: 0.6663 - acc: 0.5905 - val_loss: 0.6540 - val_acc: 0.6242
Epoch 15/1000
2497802/2497802 [==============================] - ETA: 15s - loss: 0.6656 - acc: 0.59 - ETA: 14s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6665 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5906 - ETA: 7s - loss: 0.6663 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 18s 7us/step - loss: 0.6664 - acc: 0.5906 - val_loss: 0.6543 - val_acc: 0.6242
Epoch 16/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6664 - acc: 0.59 - ETA: 12s - loss: 0.6665 - acc: 0.59 - ETA: 11s - loss: 0.6663 - acc: 0.59 - ETA: 10s - loss: 0.6663 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5905 - ETA: 7s - loss: 0.6663 - acc: 0.590 - ETA: 6s - loss: 0.6663 - acc: 0.590 - ETA: 5s - loss: 0.6662 - acc: 0.590 - ETA: 4s - loss: 0.6662 - acc: 0.590 - ETA: 3s - loss: 0.6663 - acc: 0.590 - ETA: 1s - loss: 0.6663 - acc: 0.590 - ETA: 0s - loss: 0.6663 - acc: 0.590 - 17s 7us/step - loss: 0.6663 - acc: 0.5907 - val_loss: 0.6541 - val_acc: 0.6243
Epoch 17/1000
2497802/2497802 [==============================] - ETA: 16s - loss: 0.6664 - acc: 0.59 - ETA: 14s - loss: 0.6660 - acc: 0.59 - ETA: 13s - loss: 0.6658 - acc: 0.59 - ETA: 11s - loss: 0.6662 - acc: 0.59 - ETA: 10s - loss: 0.6662 - acc: 0.59 - ETA: 9s - loss: 0.6663 - acc: 0.5902 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 6s - loss: 0.6664 - acc: 0.590 - ETA: 4s - loss: 0.6665 - acc: 0.590 - ETA: 3s - loss: 0.6666 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 18s 7us/step - loss: 0.6664 - acc: 0.5903 - val_loss: 0.6542 - val_acc: 0.6244
Epoch 18/1000
2497802/2497802 [==============================] - ETA: 14s - loss: 0.6662 - acc: 0.59 - ETA: 13s - loss: 0.6666 - acc: 0.59 - ETA: 11s - loss: 0.6664 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.59 - ETA: 9s - loss: 0.6665 - acc: 0.5901 - ETA: 8s - loss: 0.6664 - acc: 0.590 - ETA: 7s - loss: 0.6664 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6664 - acc: 0.590 - ETA: 0s - loss: 0.6664 - acc: 0.590 - 17s 7us/step - loss: 0.6664 - acc: 0.5906 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 19/1000
2497802/2497802 [==============================] - ETA: 13s - loss: 0.6673 - acc: 0.58 - ETA: 12s - loss: 0.6668 - acc: 0.59 - ETA: 11s - loss: 0.6667 - acc: 0.59 - ETA: 10s - loss: 0.6666 - acc: 0.58 - ETA: 9s - loss: 0.6666 - acc: 0.5900 - ETA: 7s - loss: 0.6666 - acc: 0.590 - ETA: 6s - loss: 0.6665 - acc: 0.590 - ETA: 5s - loss: 0.6665 - acc: 0.590 - ETA: 4s - loss: 0.6664 - acc: 0.590 - ETA: 3s - loss: 0.6664 - acc: 0.590 - ETA: 1s - loss: 0.6663 - acc: 0.590 - ETA: 0s - loss: 0.6663 - acc: 0.590 - 16s 6us/step - loss: 0.6664 - acc: 0.5905 - val_loss: 0.6540 - val_acc: 0.6244
Epoch 00019: early stopping
Wall time: 5min 12s
Out[253]:
<keras.callbacks.History at 0x26879bcb2b0>
In [254]:
%%time
# Fit the model
classifier.fit(X_train, y_train, epochs=1000, batch_size=1000000,validation_data=(X_val, y_val),callbacks=callbacks2)
Train on 2497802 samples, validate on 624451 samples
Epoch 1/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6662 - acc: 0.590 - ETA: 2s - loss: 0.6663 - acc: 0.591 - 16s 6us/step - loss: 0.6663 - acc: 0.5910 - val_loss: 0.6540 - val_acc: 0.6244
Epoch 2/1000
2497802/2497802 [==============================] - ETA: 8s - loss: 0.6661 - acc: 0.591 - ETA: 2s - loss: 0.6662 - acc: 0.590 - 15s 6us/step - loss: 0.6663 - acc: 0.5909 - val_loss: 0.6541 - val_acc: 0.6243
Epoch 3/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6665 - acc: 0.589 - ETA: 2s - loss: 0.6664 - acc: 0.590 - 16s 6us/step - loss: 0.6664 - acc: 0.5904 - val_loss: 0.6541 - val_acc: 0.6242
Epoch 4/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6662 - acc: 0.591 - ETA: 3s - loss: 0.6662 - acc: 0.590 - 16s 7us/step - loss: 0.6663 - acc: 0.5906 - val_loss: 0.6542 - val_acc: 0.6242
Epoch 5/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6664 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - 16s 6us/step - loss: 0.6664 - acc: 0.5903 - val_loss: 0.6542 - val_acc: 0.6242
Epoch 6/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6661 - acc: 0.590 - ETA: 3s - loss: 0.6663 - acc: 0.590 - 16s 6us/step - loss: 0.6664 - acc: 0.5906 - val_loss: 0.6542 - val_acc: 0.6243
Epoch 7/1000
2497802/2497802 [==============================] - ETA: 8s - loss: 0.6668 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6664 - acc: 0.5906 - val_loss: 0.6542 - val_acc: 0.6244
Epoch 8/1000
2497802/2497802 [==============================] - ETA: 9s - loss: 0.6663 - acc: 0.591 - ETA: 2s - loss: 0.6663 - acc: 0.590 - 15s 6us/step - loss: 0.6663 - acc: 0.5906 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 9/1000
2497802/2497802 [==============================] - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6664 - acc: 0.590 - 15s 6us/step - loss: 0.6665 - acc: 0.5905 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 10/1000
2497802/2497802 [==============================] - ETA: 8s - loss: 0.6663 - acc: 0.590 - ETA: 2s - loss: 0.6665 - acc: 0.590 - 15s 6us/step - loss: 0.6664 - acc: 0.5901 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 11/1000
2497802/2497802 [==============================] - ETA: 8s - loss: 0.6659 - acc: 0.591 - ETA: 2s - loss: 0.6661 - acc: 0.590 - 16s 6us/step - loss: 0.6661 - acc: 0.5908 - val_loss: 0.6541 - val_acc: 0.6244
Epoch 00011: early stopping
Wall time: 2min 51s
Out[254]:
<keras.callbacks.History at 0x267859aaa90>
In [256]:
# evaluate the model
scores = classifier.evaluate(X_val, y_val)
print("\n%s: %.2f%%" % (classifier.metrics_names[1], scores[1]*100))
624451/624451 [==============================] - ETA: 2: - ETA: 9s - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - 7s 11us/step

acc: 62.44%
In [257]:
%%time
predictions=classifier.predict(X_test)
Wall time: 11.3 s
In [258]:
%%time
#predictions_scores=model.predict_proba(X_test)
Wall time: 0 ns
In [259]:
preds=[1 if x>=0.5 else 0 for x in predictions]
In [260]:
confusion_matrix(y_test,preds)
Out[260]:
array([[424152, 244775],
       [259915, 409267]], dtype=int64)
In [261]:
accuracy_score(y_test,preds)
Out[261]:
0.6228334164107707
In [262]:
recall_score(y_test,preds)
Out[262]:
0.6115929597628149
In [263]:
precision_score(y_test,preds)
Out[263]:
0.6257503340764049
In [264]:
roc_auc_score(y_test,predictions)
Out[264]:
0.6730229533169875
In [365]:
predictions=classifier.predict(df_test[important_columns])
In [366]:
solution['HasDetections']=predictions
In [367]:
solution.to_csv('submit_31st.csv',index=False)
In [267]:
from sklearn.ensemble import AdaBoostClassifier,VotingClassifier
C:\Users\gandh\Anaconda3\lib\site-packages\sklearn\ensemble\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d
In [268]:
clf1=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='split', learning_rate=0.15, max_depth=15,
        min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=1000, n_jobs=-1, num_leaves=200, objective=None,
        random_state=42, reg_alpha=0.5, reg_lambda=0.0, silent=False,
        subsample=0.9, subsample_for_bin=200000, subsample_freq=5)
In [269]:
clf2=AdaBoostClassifier(learning_rate=0.1,n_estimators=100,random_state=42)
In [270]:
vc=VotingClassifier(estimators=[('lgb',clf1),('adb',clf2)],voting='soft',weights=[2,1])
In [271]:
import gc
gc.collect()
Out[271]:
0
In [272]:
%%time
#vc.fit(X_train[important_columns_01],y_train)
Wall time: 0 ns
In [274]:
%%time
clf2.fit(X_train,y_train)
Wall time: 31min 47s
Out[274]:
AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
          learning_rate=0.1, n_estimators=100, random_state=42)
In [275]:
predictions=clf2.predict(X_test)
prediction_scores=clf2.predict_proba(X_test)
scores=[]
for score in prediction_scores:
    scores.append(score[1])
In [276]:
print("Confusion Matrix for the Model : "+"\n",confusion_matrix(y_test,predictions))
print("Accuracy of the model : ",accuracy_score(y_test,predictions))
print("Precision of the Model : ",precision_score(y_test,predictions))
print("Recall score of the Model : ",recall_score(y_test,predictions))
print("Area under ROC curve for the Model : ",roc_auc_score(y_test,scores))
Confusion Matrix for the Model : 
 [[368338 300589]
 [212696 456486]]
Accuracy of the model :  0.6164101728633468
Precision of the Model :  0.6029600766106397
Recall score of the Model :  0.6821552283235353
Area under ROC curve for the Model :  0.6695695166372357
In [281]:
# Cross-validation
from sklearn.model_selection import KFold, StratifiedKFold, KFold #for K-fold cross validation
from sklearn.model_selection import cross_val_score #score evaluation
from sklearn.model_selection import cross_val_predict #prediction
from sklearn.model_selection import cross_validate
In [282]:
import lightgbm as lgb
In [283]:
#kf = StratifiedKFold(random_state=42,shuffle=False,n_splits=5)
In [284]:
clf=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.9,
        importance_type='split', learning_rate=0.15, max_depth=-1,
        min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
        n_estimators=1000, n_jobs=-1, objective=None,
        random_state=42, reg_alpha=0.7, reg_lambda=0.7, silent=False,
        subsample=0.9,subsample_freq=5)
In [288]:
param = {'objective':'binary',
         'boosting': 'gbdt',     
         'learning_rate': 0.05,
         'max_depth': -1,
         'num_leaves': 100,
         'sub_feature': 0.9,
         'sub_row':0.9,
         'lambda_l1': 0.6,
         'lambda_l2': 0.6,
         "random_state": 133,
         "verbosity": -1}
In [289]:
features=X.columns.tolist()
import gc
In [290]:
%%time
max_iter = 5
#categorical_columns = [c for c in categorical_columns if c not in ['MachineIdentifier']]
#features = [c for c in train.columns if c not in ['MachineIdentifier']]
gc.collect()

folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=15)
oof = np.zeros(len(X))
predictions = np.zeros(len(df_test))
feature_importance_df = pd.DataFrame()
score = [0 for _ in range(folds.n_splits)]

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X,y.values)):
    print("fold {}".format(fold_))
    trn_data = lgb.Dataset(X.iloc[trn_idx][features], label=y.iloc[trn_idx])
    val_data = lgb.Dataset(X.iloc[val_idx][features], label=y.iloc[val_idx])

    num_round = 1000
    clf = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=-1, early_stopping_rounds = 200)
    oof[val_idx] = clf.predict(X.iloc[val_idx][features], num_iteration=clf.best_iteration)
    
    fold_importance_df = pd.DataFrame()
    fold_importance_df["feature"] = features
    fold_importance_df["importance"] = clf.feature_importance(importance_type='gain')
    fold_importance_df["fold"] = fold_ + 1
    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
    
    # we perform predictions by chunks
    initial_idx = 0
    chunk_size = 1000000
    current_pred = np.zeros(len(df_test))
    
    while initial_idx < df_test.shape[0]:
        final_idx = min(initial_idx + chunk_size, df_test.shape[0])
        idx = range(initial_idx, final_idx)
        current_pred[idx] = clf.predict(df_test.iloc[idx][features], num_iteration=clf.best_iteration)
        initial_idx = final_idx
    
    predictions += current_pred / min(folds.n_splits, max_iter)
    score[fold_] = roc_auc_score(y.iloc[val_idx], oof[val_idx])
    if fold_ == max_iter - 1: break
        
if (folds.n_splits == max_iter):
    print("CV score: {:<8.5f}".format(roc_auc_score(y, oof)))
else:
     print("CV score: {:<8.5f}".format(sum(score) / max_iter))
fold 0
Training until validation scores don't improve for 200 rounds.
Did not meet early stopping. Best iteration is:
[1000]	training's binary_logloss: 0.596156	valid_1's binary_logloss: 0.604861
fold 1
Training until validation scores don't improve for 200 rounds.
Did not meet early stopping. Best iteration is:
[1000]	training's binary_logloss: 0.595986	valid_1's binary_logloss: 0.60519
fold 2
Training until validation scores don't improve for 200 rounds.
Did not meet early stopping. Best iteration is:
[1000]	training's binary_logloss: 0.596275	valid_1's binary_logloss: 0.605196
fold 3
Training until validation scores don't improve for 200 rounds.
Did not meet early stopping. Best iteration is:
[1000]	training's binary_logloss: 0.596253	valid_1's binary_logloss: 0.604526
fold 4
Training until validation scores don't improve for 200 rounds.
Did not meet early stopping. Best iteration is:
[1000]	training's binary_logloss: 0.59619	valid_1's binary_logloss: 0.604496
CV score: 0.72831 
Wall time: 1h 35min 17s
In [293]:
cols = (feature_importance_df[["feature", "importance"]]
        .groupby("feature")
        .mean()
        .sort_values(by="importance", ascending=False)[:1000].index)

best_features = feature_importance_df.loc[feature_importance_df.feature.isin(cols)]

plt.figure(figsize=(14,25))
sns.barplot(x="importance",
            y="feature",
            data=best_features.sort_values(by="importance",
                                           ascending=False))
plt.title('LightGBM Features (avg over folds)')
plt.tight_layout()
plt.savefig('lgbm_importances2.png')
In [294]:
#solution=pd.DataFrame()
In [295]:
#solution['MachineIdentifier']=df_test['MachineIdentifier']
In [296]:
solution['HasDetections']=predictions
In [297]:
solution.to_csv('submit_31st.csv',index=False)